Face Recognition Vendor Test (FRVT)

Performance of Face Identification Algorithms

NIST Interagency Report 8009

Patrick Grother Mei Ngan

Information Access Division

National Institute of Standards and Technology

May 26, 2014
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 1

Acknowledgements

The authors would like to thank the sponsors of this activity. These are the Criminal Justice Information

Systems (CJIS) division and the Biometric Center of Excellence (BCOE) of the Federal Bureau of Investigation,

the Science and Technology (S&T) Directorate in the Department of Homeland Security (DHS), and the Office

of Biometric Identity Management (OBIM) office, also in DHS.

Disclaimer

Specific hardware and software products identified in this report were used in order to perform the evaluations

described in this document. In no case does identification of any commercial product, trade name, or vendor,

imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it

imply that the products and equipment identified are necessarily the best available for the purpose.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 2

Executive Summary

Purpose and scope: This report documents the performance of one-to-many face recognition algorithms, and compares

it with that measured in 2010. Performance in this context refers to recognition accuracy and computational resource

usage as measured by executing those algorithms on massive sequestered datasets. These are: reasonable quality law

enforcement mugshot images; poor quality webcam images collected in similar detention operations; and moderate quality visa application images. The mugshot and visa images are used to approximate performance obtainable using high

quality ISO standardized images collected in passport, visa and driving license duplicate detection operations. These applications constitute the largest revenue segment in the face recognition marketplace. The webcam images are included

to show how recognition accuracy degrades in non-ideal poorly-controlled situations - results will mimic recognition in

adverse e.g. surveillance situations to the extent that those applications produce images similar to the webcam set.

Out of scope: Not within the scope of this report are: performance of live transactional systems like automated border

control gates; human recognition accuracy as used in forensic applications; and recognition of persons in video sequences

(which NIST is evaluating separately and will report on later). Some of those applications are likely to share technologies

that are tested in this report.

Participation: The report includes performance figures for prototype algorithms from the research laboratories of many

of the major commercial suppliers of face recognition technologies. It thereby facilitates robust comparative evaluation.

However, while participation in the test was open to any organization worldwide, neither social media companies nor

most academic institutions elected to submit algorithms, and this report therefore does not capture their technical capabilities except to the extent that those technologies have been adopted or licensed by FRVT participants.

Background: Face recognition error rates have declined massively in the two decades since initial commercialization of

the various technologies. NIST has tracked that improvement and its conduct of regular independent, free, open, and

public evaluations has fostered improvements in the state of the art. One-to-many face identification systems are mostly

used in conjunction with trained human facial reviewers. The systems are configured to operate in two regimes: first,

with a low threshold that necessitates adjudication of many false positive candidates by a reviewer specifically employed

to do so; second, with a high threshold, in which false positive outcomes are rare and human intervention is only needed

infrequently. Low false positive rates are accompanied by higher false negative rates - this report includes extensive

quantification of this tradeoff.

Results summary: Since NIST’s last evaluation was published in August 2010, the algorithms from NEC remain the most

accurate followed by those of Morpho which merged its algorithms with those acquired from L1 Identity Solutions in 2011.

Thereafter Toshiba, Cognitec Systems, and 3M/Cogent constitute the leading commercial suppliers. Algorithms with

lesser levels of capability are those from Neurotechnology, Zhuhai Yisheng, HP, Decatur, and Ayonix. Importantly, however, performance is not single-faceted and any ranking of performance across algorithms must be weighed by applicationspecific requirements. For example, some algorithms are more suited to recognition of difficult webcam images; and the

search speed of some algorithms increases only slowly with enrolled population size.

The headline results follow in the Technical Summary.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 3

Technical Summary

Absolute accuracy: When mugshot images from 1.6 million individuals are enrolled by the most accurate algorithm,

4.1% of subsequent mated searches fail to yield the correct mate in rank one position. Practically this result assumes

(a) Good quality mugshot

(b) Poor quality webcam

Figure 1: Examples of images used in this report.

that a human reviewer will be employed to adjudicate the candidate identities. If, on the other hand, a threshold is elevated to

limit false positive outcomes to only 1 in 500 searches (0.2%), the

failure to find the mate rises to 7.5%. Using poorly constrained

webcam images, which exhibit serious departures from most

quality-related clauses of published image standards, identification miss rates are typically between two and five times

higher such that the correct mate is not found at rank one in

20-60% of searches. Exceptionally, however, the most accurate

algorithm fails in only 11.3% of searches. This latter result is notable in that it indicates that face recognition can work on nonideal images. Thus, while the webcam images were collected

from nominally cooperative subjects, the use of inferior equipment and procedures means, for example, that images from

non-cooperative bank ATM machine and surveillance camera

deployments will be recognizable in a useful number of cases.

Such outcomes have been reported operationally [19] and in the laboratory [17].

Accuracy across commercial providers: Recognition accuracy is very strongly dependent on the algorithm and, more

specifically, on the developer of the algorithm. Recognition error rates in a particular scenario range from a few percent up

to beyond fifty percent. Among the most accurate developers, the rank one miss rates for recognition in a population size

of 1.6 million are 4.1% (NEC), 9.1% (Morpho), 10.7% (Toshiba), 13.6% (Cognitec), 17.2% (3M) and 20.5% (Neurotechnology). For webcam images, this sequence is 11.3% (NEC), 23.7% (Toshiba), 29.8% (Morpho), 36.4% (3M), 57.6% (Cognitec)

and 66.9% (Neurotechnology). While results for up to six algorithms from each developer are reported here, the intraprovider accuracy variations are usually smaller than the inter-provider variations. That said, some developers submitted

different, less accurate but computationally lightweight algorithms.

Accuracy under increasing population size: As more identities are enrolled into a biometric system, the possibility of

a false positive increases due to lookalike faces that yield extreme values in the tail of the nonmate score distribution.

However these scores are lower than most mate scores such that when an identification is configured with a threshold

of zero, and where human adjudication is always necessary, rank-one identification miss rates scale very favorably with

population size, N, growing approximately as a power law, aNb. Depending on the algorithm, the exponent b for mugshot

searches is low, on the range [0.08, 0.16] meaning that a large, 10-fold, increase in N, yields only a 1.2 to 1.4 fold increase

in the rate at which mated searches do not yield the correct mate. Thus the rank one mugshot miss rates, for search into a

database of just 160,000, are only modestly improved over those at 1.6 million: 4.1% to 3.4% (NEC), 9.1% to 7.6% (Morpho),

10.7% to 7.9% (Toshiba), 13.6% to 10.9% (Cognitec), 17.2% to 13.3% (3M) and 20.5% to 16.9% (Neurotechnology). While

extrapolation to larger populations is technically problematic, face identification systems will be useful, if imperfect, in

nation-state population sizes.

Utility of adjudicating long candidate lists: In the regime where a system is configured with a threshold of zero, and where

human adjudication is always necessary, the reviewer will find mates on candidate lists at ranks far from one. The accuracy

benefits of traversing such lists are usually substantial: For example, in a population of 1.6 million, the rank one and rank

fifty miss rates are 4.1% to 2.6% (NEC), 9.1% to 7.1% (Morpho), 10.7% to 5.7% (Toshiba), 13.6% to 8.4% (Cognitec), 17.2%

to 9.8% (3M) and 20.5% to 13.6% (Neurotechnology). These are diminishing returns, however, with miss rates for some

algorithms growing as power-law, aRc, in the number of candidates a reviewer is willing to consider, R. The exponent c

for mugshot searches is typically on the range [−0.2, −0.1] indicating that rank 1 miss rates are reduced by from 20% to

40% if 10 candidates are available for inspection, but only by 30% to 50% if 50 are considered.

Human adjudication workload: Human reviewers will typically only need to search the first few (highest scoring) candidates returned in a search. The expected number of comparisons constitutes a workload measure which can be used

to both compare algorithms and to inform operational labor requirements. If a score threshold is applied to reduce the

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 4

length of candidate lists, substantial reviewer workload reductions can be realized but at the expense of increased miss

rates. In the best case, application of a threshold to candidates from a Morpho algorithm reduced workload by 60% with

only a factor of 1.05 more misses than without a threshold.

Accuracy by age group: Identification accuracy is strongly dependent on subject age. For all algorithms, older individuals

are both easier to recognize as themselves, and easy to tell apart from each other. The opposite is true in children: both

false negative and false positive rates are much higher, with infants being very hard to identify. Moreover, the trends are

progressive throughout adulthood, with young adults being identified with worse accuracy than older. These results are

derived over images taken, on average, 2-3 years apart so the effects of ageing (craniofacial shape change) are influential

only on the results for younger individuals.

Accuracy gains 2010-2013: For the four developers who submitted algorithms to NIST in 2010 and 2013, accuracy has

improved in all cases. Rank one miss rates have reduced by about 10% for Cognitec, Neurotechnology, and Morpho, and

by about 28% for NEC (from 8.9% to 6.4%). More substantial reductions have been realized when a threshold is applied to

permit false positive outcomes in only 1 in 500 nonmate searches: There Morpho realizes a 21% reduction in misses (from

24.8% to 19.5%), while for NEC, the figure is 60% (26.9% to 10.8%). Finally the accuracy gains when reviewers examine up

to 50 candidates are more modest, ranging from 3% (Cognitec) to 12% (Neurotechnology).

Sketch recognition: By searching a non-operational set of sketch images against photographs seeded into a population

of 640,000 nonmated mugshots, the most accurate algorithms produce the mated photograph only infrequently: The

mate is not among the top 50 candidates at the following rates: 73.3% (3M/Cogent), 73.8% (NEC), 78.5% (Toshiba), 80.3%

(Morpho), and 81.5% (Neurotechnology). While these error rates are very high, they are neverthess valuable in developing

investigative leads in cases which are otherwise cold. An important caveat is that sketch-identification was never declared

to be part of FRVT and better algorithms may be available from the providers. That said, face recognition algorithms are

being used to recognize sketches operationally. Further accuracy will clearly be dependent on eye-witness recall, artist

interpretation if any, and software interfaces.

Conclusions: As with other biometrics, accuracy of facial recognition implementations varies greatly across the industry.

Absent other performance or economic parameters, users should prefer the most accurate algorithm. Note, however, that

the results of this section are entirely rank-based befitting use of face recognition in the investigational mode in which

an reviewer is willing to traverse candidate lists looking for mates. Subsequent investigations in this report consider

threshold-based metrics appropriate for identification mode applications. Note that the absolute values of identification

accuracy will always depend on the dataset used, specifically to the properties of the images in use. In particular, the main

dataset used here includes some images that are not perfectly frontal, such that conformance to the appearance-related

requirements of the ISO/IEC 19794-5 “gold” standard is imperfect.

Improving face recognition: On the basis of the results in this report, we identify the following drivers of overall recognition accuracy, and quantify their relative influence relative on false negative miss rates.

. Quality: Improvement of image quality is the largest contributing factor to recognition accuracy. Results in this

report note a four fold reduction in miss rates using mugshots vs. webcam images. Further improvements in

accuracy can be obtained by enhancing conformance to the ISO/IEC 19794-5 “gold” standard [26], particularly using

two complementary approaches: by design via improved optical and photographic aspects, and by careful application

of compression algorithms; and by detection of non-conformant (for example, blurred or non-frontal) images at the

time they are collected. A new program has been established to evaluate algorithms capable of detecting defective

images [9, 10]. The best practice should be to collect and retain forensic quality photographs (i.e. subject acquisition

profile 50/51 instances of the ANSI/NIST Type 10 standard [27]), from which ISO/IEC 19794-5 images should be

prepared for automated recognition.

. Human adjudication of candidate lists: Recognizing that a human is often involved in examining candidates produced in an automated one-to-many search, it is imperative that the enrolled reference “exemplar” image be of high

quality. Given ready availablity of high resolution digital cameras, the ANSI/NIST standard advocates collection of

forensic quality images for which the interocular distance is around 800 pixels. Such images are not used directly for

automated face recognition, so the default guidance is to collect and retain the forensic image, and to prepare from

it the ISO standard image (120 pixels interocular distance) for automated face recognition.

Accuracy can be improved by supplementing automated facial identification with human adjudication of the canA = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 5

didate photographs. Particularly by traversing long candidate lists (for example, up to length 50) miss rates can

readily be reduced by a factor of two (i.e. 50%) An important caveat here is that the accuracy with which human

reviewers can reliably adjudicate the most-similar faces returned in a large-population one-to-many search remains

poorly quantified.

. Algorithm selection: Algorithm performance varies substantially - even among the leading providers a factor of two

reduction in identification miss rates can be realized by replacing one algorithm with a better one. Such a step should

be undertaken only while paying due attention to other performance related factors, such as computational expense

and impostor distribution stability.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 6

Release Notes

. FRVT Tracks: NIST initiated FRVT in the second half of 2012. We invited participation in five tracks.

→ Class A: Accuracy of algorithms executing one-to-one verification comparisons to determine if two samples

originate from the same person or not.

→ Class B: Accuracy of algorithms executing one-to-one verification but with an enrollment database present.

This track was discontinued after the 2010 evaluation. Accuracy gains over class A are available.

→ Class C: Accuracy of algorithms executing one-to-many identification searches to determine either that the

person is not enrolled, or to determine the identity of the person.

→ Class D: Accuracy of algorithms tasked with determining the sex or age of a person in one or more input

images. A separate class D track tasked algorithms to determine whether a face in an image is frontal to the

camera or not.

→ Class F: Effectiveness of algorithms that take one or more (non-frontal) input images of a person, and produce

one (or more) frontally posed images of that person.

→ Class V: Effectiveness of algorithms that execute one-to-many identification of persons whose faces appear

in frames extracted from video surveillance sequences.

This report details results only for class C algorithms.

. FRVT Reports: The results of the FRVT appear as a series of NIST Interagency Reports. The reports were developed separately and released on different schedules. In prior years NIST has mostly reported FRVT results as

a single report; this had the disadvantage that results from completed sub-studies were not published until all

other studies were complete.

All reports are linked from http://face.nist.gov/frvt and its sub-pages.

. Appendices: This report is accompanied by a number of appendices which present exhaustive results on a peralgorithm basis. These are machine-generated and are included because the authors believe that visualization of

such data is broadly informative and vital to understanding the context of the report.

. Typesetting: Virtually all of the tabulated content in this report was produced automatically. This involved

the use of scripting tools to generate directly type-settable LATEX content. This improves timeliness, flexibility,

maintainability, and reduces transcription errors.

. Graphics: Many of the Figures in this report were produced using both Deepayan Sarkar’s Lattice package [22]

and Hadley Wickham’s ggplot2 package running under R, the capabilities of which extend beyond those evident

in this document.

. Contact: Correspondence regarding this report should be directed to PGROTHER at NIST dot GOV.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 7

Contents

ACKNOWLEDGEMENTS 1

DISCLAIMER 1

EXECUTIVE SUMMARY 2

TECHNICAL SUMMARY 3

RELEASE NOTES 6

1 INTRODUCTION 11

2 PARTICIPATION 11

3 EVALUATION DATASETS 12

3.1 IMAGE TYPES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 ENROLMENT TYPES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 LIMITATIONS OF THE DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4 PERFORMANCE METRICS 16

4.1 QUANTIFYING FALSE ALARMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.2 QUANTIFYING HITS AND MISSES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.3 BEST PRACTICE TESTING REQUIRES EXECUTION OF SEARCHES WITH AND WITHOUT MATES . . . . . . . . . . . . . . . . . 18

4.4 FAILURE TO EXTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.5 TIMING MEASUREMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.6 UNCERTAINTY ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5 RESULTS 20

5.1 COMPARATIVE ACCURACY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.2 ACCURACY DEPENDENCE ON RANK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.3 EFFECT OF POPULATION SIZE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.4 EFFECT OF ENROLLING ALL HISTORICAL IMAGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.5 ACCURACY DEPENDENCE ON SUBJECT AGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.6 ACCURACY OF SKETCH RECOGNITION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.7 HUMAN WORKLOAD FOR CANDIDATE LIST ADJUDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.8 IMPOSTOR DISTRIBUTION STABILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.9 COMPUTATIONAL EXPENSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.10 TEMPLATE SIZE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.11 EXPLOITING MULTIPLE CORES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

A BIOMETRIC ERROR RATE TRADEOFF CHARACTERISTICS 54

A ALGORITHM REPORT CARDS 58

A ALGORITHM ACCURACY BY AGE GROUP 98

List of Tables

1 PARTICIPANTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 IMAGE PROPERTIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 ENROLMENT AND SEARCH SETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4 PERFORMANCE WITH N = 160,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5 PERFORMANCE WITH N = 640,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

6 PERFORMANCE WITH N = 1,600,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

7 ACCURACY GAINS 2010 TO 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

8 ENROLMENT AND SEARCH SETS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

9 MUGSHOT VS. WEBCAM INTEROPERABILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 8

10 MISS RATE VS. POPULATION SIZE AT RANK 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

11 MISS RATE VS. POPULATION SIZE AT RANK 50 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

12 POWER-LAW MODELS OF CUMULATIVE MATCH CHARACTERISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

13 AGE GROUPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

14 SKETCH VS. PHOTO IDENTIFICATION ACCURACY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

15 EFFECT OF POPULATION SIZE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 9

List of Figures

1 IMAGE QUALITY MATTERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 IMAGES OF THE LEO DATASET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3 FERET IMAGE AND CORRESPONDING SKETCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4 MULTIPLE-IMAGE ENROLLMENT TYPES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

5 SELECTIVITY VS. FALSE POSITIVE IDENTIFICATION RATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

6 ALGORITHM COMPARISON: DET CHARACTERISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

7 MUGSHOT VS. WEBCAM INTEROPERABILITY CMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

8 EFFECT OF RANK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

9 MISS RATES VS. POPULATION SIZE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

10 MISS RATE VS. POPULATION SIZE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

11 EFFECT OF AGE GROUP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

12 REVIEWER WORKLOAD REDUCTIONS WITH THRESHOLDING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

13 PERFORMANCE TRADESPACES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

14 DET PROPERTIES AND INTERPRETATION :: ERROR RATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

15 DET PROPERTIES AND INTERPRETATION :: NON-IDEAL TESTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

16 DET PROPERTIES AND INTERPRETATION :: ALGORITHMS USED IN COMBINATION . . . . . . . . . . . . . . . . . . . . . . 57

17 KEY TO REPORT CARD FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

18 PERFORMANCE REPORT A20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

19 PERFORMANCE REPORT A30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

20 PERFORMANCE REPORT A31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

21 PERFORMANCE REPORT A32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

22 PERFORMANCE REPORT B30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

23 PERFORMANCE REPORT B31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

24 PERFORMANCE REPORT B32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

25 PERFORMANCE REPORT B33C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

26 PERFORMANCE REPORT C20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

27 PERFORMANCE REPORT C30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

28 PERFORMANCE REPORT C31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

29 PERFORMANCE REPORT C32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

30 PERFORMANCE REPORT D20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

31 PERFORMANCE REPORT D30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

32 PERFORMANCE REPORT D31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

33 PERFORMANCE REPORT D32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

34 PERFORMANCE REPORT D33C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

35 PERFORMANCE REPORT D34C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

36 PERFORMANCE REPORT E20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

37 PERFORMANCE REPORT E21C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

38 PERFORMANCE REPORT E30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

39 PERFORMANCE REPORT E31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

40 PERFORMANCE REPORT F20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

41 PERFORMANCE REPORT F30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

42 PERFORMANCE REPORT F31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

43 PERFORMANCE REPORT G30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

44 PERFORMANCE REPORT G31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

45 PERFORMANCE REPORT H30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

46 PERFORMANCE REPORT J20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

47 PERFORMANCE REPORT J30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

48 PERFORMANCE REPORT J31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

49 PERFORMANCE REPORT J32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

50 PERFORMANCE REPORT J33C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

51 PERFORMANCE REPORT L30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

52 PERFORMANCE REPORT L31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

53 PERFORMANCE REPORT M30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

54 PERFORMANCE REPORT P30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

55 PERFORMANCE REPORT Q30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 10

56 PERFORMANCE REPORT T30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

57 EFFECT OF AGE A20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

58 EFFECT OF AGE A30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

59 EFFECT OF AGE A31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

60 EFFECT OF AGE A32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

61 EFFECT OF AGE B30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

62 EFFECT OF AGE B31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

63 EFFECT OF AGE B32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

64 EFFECT OF AGE B33C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

65 EFFECT OF AGE C20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

66 EFFECT OF AGE C30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

67 EFFECT OF AGE C31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

68 EFFECT OF AGE C32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

69 EFFECT OF AGE D20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

70 EFFECT OF AGE D30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

71 EFFECT OF AGE D31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

72 EFFECT OF AGE D32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

73 EFFECT OF AGE D33C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

74 EFFECT OF AGE D34C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

75 EFFECT OF AGE E20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

76 EFFECT OF AGE E21C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

77 EFFECT OF AGE E30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

78 EFFECT OF AGE E31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

79 EFFECT OF AGE F20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

80 EFFECT OF AGE F30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

81 EFFECT OF AGE F31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

82 EFFECT OF AGE G30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

83 EFFECT OF AGE G31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

84 EFFECT OF AGE H30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

85 EFFECT OF AGE J20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

86 EFFECT OF AGE J30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

87 EFFECT OF AGE J31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

88 EFFECT OF AGE J32C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

89 EFFECT OF AGE J33C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

90 EFFECT OF AGE L30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

91 EFFECT OF AGE L31C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

92 EFFECT OF AGE M30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

93 EFFECT OF AGE Q30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

94 EFFECT OF AGE S20C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

95 EFFECT OF AGE T30C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 11

Provider Letter Number of algorithms submitted

Name Code Phase 1 Phase 2 Phase 3 Total

3M/Cogent A 3 1 3 7

Cognitec B 1 0 4 5

Neurotechnology C 3 1 3 7

Safran Morpho D 1 1 5 7

NEC E 1 2 2 5

Tsinghua University (EE - Prof. Wen) F 2 3 2 7

Beijing Ivsign Technology Co. Ltd. G 2 1 2 5

Chinese Academy of Sci. - Inst. Automation (Prof. Liu) H 1 0 1 2

Chinese Academy of Sci - Inst. Computing Technology (Prof. Shan) I 2 1 0 3

Toshiba Corporation J 2 2 3 7

Tsinghua University (EE - Prof. Su) L 3 2 2 7

HP / Virage M 1 2 1 4

Zhuhai Yisheng Electronics Tech. Co. Ltd. P 2 0 1 3

JunYu Technology Co. Ltd. Q 3 1 1 5

Decatur Industries Inc. S 0 1 0 1

Ayonix Inc. (JP) T 0 0 1 1

Table 1: Algorithm submissions by provider The number of different algorithms (i.e. libraries and ancillary files) submitted to FRVT by

Phase, and in total. Phase 1 closed in October 2012, Phase 2 in March 2013, and Phase 3 in October 2013. Algorithms are identified in

this report by the letter code in column 2, the phase it was submitted, and a sequence number starting from zero. This report does not

include results for Phase 1 algorithms. NIST limited the total number of algorithms to 7.

1 Introduction

One-to-many identification represents the largest market for face recognition technology. Algorithms are used across the

world in a diverse range of biometric applications: detection of duplicates in databases, detection of fraudulent applications for credentials such as passports and driving licenses, token-less access control, surveillance, social media tagging,

lookalike discovery, criminal investigation, and forensic clustering.

FRVT tested only open-set identification algorithms because real-world applications are almost always “open-set”. This

means that some searches have an enrolled mate, but some do not. For example, some subjects have truly not been issued

a visa or drivers license before; some law enforcement searches are from first-time arrestees1. In an “open-set” application,

algorithms make no prior assumption about whether or not to return a high-scoring result, and for a mated search, the

ideal behaviour is that the search produces the correct mate at high score and first rank. For a nonmate search, the ideal

behavior is that the search produces zero high-scoring candidates.

In most applications, the core accuracy of a facial recognition algorithm is the most important performance variable.

Resource consumption will be important also as it drives the amount of hardware, power, and cooling necessary to accomodate workflows. Algorithms consume processing time, they require computer memory, and their static template data

requires storage space. This report documents all of these variables.

2 Participation

The organizations listed in Table 1 elected to submit algorithm to the FRVT evaluation.

1Operationally closed-set applications are rare because it is usually not the case that all searches have an enrolled mate. One counter-example, however, is

a cruise ship in which all passengers are enrolled and all searches should produce one, and only one, identity. Another example is forensic identification

of dental records from an aircraft crash.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 12

Property LEO/Mugshot LEO/Webcam VISA

Collection Environment Law enforcement booking Border detainee booking Visa application process

Collection Era  ̃1960s-2008  ̃2006-2010  ̃2006-2010

Camera Digital, live Webcam, live Scanned paper, some live

Documentation NIST Special Db 32 Vol 1 NIST Special Db 32 Vol 1 http://travel.state.gov/visa/visa 1750.html

Image dimensions Various, 480x640, 240x240,

768x960

240x240 Mostly 252x300

Compression JPEG  ̃20:1, mean-size: 48kB JPEG, mean size: 5.7kB JPEG, mean size: 9.2kB

Eye to eye distance Mean = 107 pixels, SD = 40 pixels Mean = 45 pixels, SD = 12 Mean = 67 pixels, SD = 6

Frontal pose Moderate control Poor control Well controlled

Full frontal geometry Mostly, but varying torso

visibility

Rarely, cluttered, large, bright

backgrounds

Good but cropped more closely than ISO

full-frontal

Parent Operational data Operational data Operational data

Population US, adult Central America, adult Global, adult + children

Table 2: Image sources and properties. The table summarizes the key characteristics of the images used in this study.

3 Evaluation datasets

3.1 Image types

LEO images: As in our last evaluation, this report primarily reports results for an operational dataset of law enforcement images, referred to as LEO. Additionally it employs a new dataset of VISA images. The properties of these sets is

summarized in Table 2. As shown in Figure 2, the LEO set contains images of two distinct types:

. Mugshots: Comprising about 86% of the LEO database, are mugshots having reasonable compliance with the

ANSI/NIST ITL 1-2011 Type 10 standard’s subject acquisition profiles levels 10-20 for frontal images [27]. The major

departure from the standard’s requirements is the presence of mild pose variations around frontal - the images of

Figure 2 are typical. The images vary in size, with many being 480x600 pixels with JPEG compression applied to

produce filesizes of between 18 and 36KB with many images outside this range, implying that about 1.25 bits are

being encoded per color pixel.

. Webcam images: The remaining 14% of the images were collected using an inexpensive webcam attached to a

flexible operator-directed mount. These images are all of size 240x240 pixels, that are in considerable violation of

most quality-related clauses of all face recognition standards. As evident in the figure, the most common defects

are non-frontal pose (associated with the rotational degrees of freedom of the camera mount), low contrast (due to

varying and intense background lights), and poor spatial resolution (due to inexpensive camera optics). The images

are overly JPEG compressed, to between 4 and 7KB, implying that only 0.5 to 1 bits are being encoded per color pixel.

In our 2010 report [12], accuracy was only stated for the aggregrate LEO dataset. Here, for the first time, we report accuracy

separately for the mugshots and the webcams images.

Visa images: In addition, we utilize a smaller visa database that has not been used in prior NIST evaluations. It consists

of very well controlled frontal photographs of adults and children born worldwide. However, some of the images are

scanned from paper photographs and therefore exhibit a reduced optical resolution. Further, as noted in Table 2, the

images have a low mean interocular distances (IOD) of 67 pixels and compressed sizes of 9.2KB. These values are below the preferred minimums for the “Token” image format indicated in the ISO/IEC 19794-5:2005 standard for modern

credentials, namely 120 pixels interocular distance, filesize of at least 15KB, and an image size of 480x640 pixels.

FERET Sketch images: The FERET database was collected in the 1990s and has been very widely studied. As such it is

not used for evaluation here. However the City University of Hong Kong employed an artist to produce, for each person,

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 13

(a) Webcam

(b) Mugshot

Figure 2: Examples of the two kinds of image that comprise the LEO database.

Figure 3: Sketch realism: A pair of images of an individual in the FERET database. At left is the grayscale version of the original 1994

frontal image. At right is the artists sketch of the grayscale image. This production of a sketch is atypical operationally: it is unusual for

an artist to have access to an image of the individual.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 14

Image

Encounter 1 . . . Ki − 1 Ki

Capture Time T1 . . . TKi−1 TKi

Role RECENT Not used Not used Enrolled Search

Role LIFETIME Enrolled Enrolled Enrolled Search

Figure 4: Depiction of the “recent” and “lifetime” enrollment types.

“a face photo with lighting variation and a sketch with shape exaggeration drawn by an artist when viewing this photo.”

The data was collected to support research into synthesis and recognition of sketches [25, 28]. An example is shown in

Figure 3.

3.2 Enrolment types

Many operational applications include collection and enrolment of biometric data from subjects on more than one occasion. This might be done on a regular basis, as might occur in credential (re-)issuance, or irregularly, as might happen in

a criminal recidivist situation [3]. The number of images per person will depend on the application area: In civil identity

credentialing (e.g. passports, driving licenses), the images will be acquired approximately uniformly over time (e.g. ten

years for a German passport). While the distribution of dates for such images of a person might be assumed uniform, a

number of factors might undermine this assumption2. In criminal applications, the number of images would depend on

the number of arrests. The distribution of dates for arrest records for a person (i.e. the recidivism distribution) has been

modeled using the exponential distribution but is recognized to be more complicated3.

In any case, the 2010 NIST evaluation of face recognition showed that considerable accuracy benefits accrue with retention

and use of all historical images [12].

To this end, the FRVT API document provides K ≥ 1 images of an individual to the enrolment software. The software is

tasked with producing a single proprietary undocumented “black-box” template4from the K images. This affords the

algorithm an ability to generate a model of the individual, rather than to simply extract features from each image on a

sequential basis.

As depicted in Figure 4, the i-th individual in the LEO dataset has Kiimages. These are labelled xk for k = 1 . . . Ki. To

measure the utility of having multiple enrolment images, this report evaluates two kinds of enrolment:

. Recent: Only the second most recent image, xKi−1 is enrolled. This type of enrolment mimics the operational policy

of retaining the imagery from the most recent encounter. This might be done operationally to ameliorate the effects

of face ageing.

. Lifetime: All except the last image are enrolled, x1 . . . xKi−1. This strategy might be adopted if quality variations

exist where an older image might be more suitable for matching, despite ageing.

2For example, a person might skip applying for a passport for one cycle, letting it expire. In addition, a person might submit identical images (from the

same photography session) to consecutive passport applications at five year intervals.

3A number of distributions have been considered to model recidivism, see for example [2].

4There are no formal face template standards. Template standards only exist for fingerprint minutiae - see ISO/IEC 19794-2:2011.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 15

Enrolment Search

Type Num. IDs Num. Images Num. Images and IDs

See Mate Nonmate

See sec. 3.2 N Webcam Mugshot Total Webcam Mugshot Webcam Mugshot

1 RECENT 20,000 0 20,000 20,000 0 20,000 28,936 171,066

2 RECENT 160,000 22,886 137,114 160,000 10,660 50,000 28,936 171,066

3 RECENT 640,000 81,221 558,779 640,000 10,660 50,000 28,936 171,066

4 RECENT 1,600,000 196,885 1,403,115 1,600,000 10,660 50,000 28,936 171,066

5 LIFETIME 20,000 0 32,948 32,948 0 20,000 28,936 171,066

6 LIFETIME 160,000 25,726 199,859 225,585 10,660 50,000 28,936 171,066

7 LIFETIME 640,000 87,864 769,070 856,934 10,660 50,000 28,936 171,066

8 LIFETIME 1,600,000 210,693 1,907,057 2,117,750 10,660 50,000 28,936 171,066

Image Type Enrol Type Num. IDs Num. enrol images Num. mate searches Num. nonmate searches

12 VISA RECENT 19,972 19,972 19,972 203,082

Table 3: Enrolment and search sets. Each row summarizes one identification trial. The upper table concerns use of the LEO images; the

lower, the VISA images. The column labeled “Num. IDs” gives the number of enrolled identities. This precedes the numbers of images,

and then the number of mate, and nonmate, searches. Rows 1-8 describe trials in which webcam and mugshot images are enrolled in

the natural proportions. Row 12 refers to the visa database that, uniquely, contains images of children.

In all cases, the most recent image, xKi, is reserved as the search image. For the 1.6 million subject enrolment parition of

the LEO data, 1 ≤ Ki ≤ 33 with Ki = 1 in 80.1% of the individuals, Ki = 2 in 13.4%, Ki = 3 in 3.7%, Ki = 4 in 1.4%,

Ki = 5 in 0.6%, Ki = 6 in 0.3%, and Ki > 6 is 0.2% for everyone else. This distribution is substantially dependent on

United States recidivism rates.

We did not evaluate the case of retaining only the highest quality image, since automated quality assessment is out of scope

for this report. We do not anticipate that such strategies will prove beneficial when the quality assessment apparatus is

imperfect and unvalidated.

Finally, we did not evaluate the case where Ki > 1 images from the same person are enrolled under different identifiers.

This very common circumstance arises in so-called “event-based” applications where no attempt is made to consolidate

images of a person into a single identity. Searches against such systems are likely to yield more than one image of a person

in the top ranks. We do not test this kind of enrolment. Instead, we use our consolidated identity design to: a) realize

accuracy gains by affording the algorithm the opportunity to build a holistic model of identity (as is common in speaker

recognition systems); and b) to simplify accuracy measurement.

3.3 Limitations of the data

Neither the mughots nor the visa images have ideal properties. The former, particularly, has too much pose variation,

and the latter is degraded by its acquistion process (scan-from-paper) and too much JPEG compression. A prior NIST

report [21] documents the dependence of recognition accuracy on compression and spatial sampling rate (eye-to-eye

distance). While results for the LEO/Mugshot images are the best indicators of the accuracy that can be expected in

contemporary deployments of one-to-many identification, it should be understood that accuracy increases can be realized

by improving conformance to the appearance-related requirements of the ISO/IEC 19794-5 standard [11]

5

. That said,

conformance to that standard requires both deliberate system design (capture equipment, capture environment, image

preparation processes), and capture-time monitoring (to check for aberrant subject behavior). This latter aspect will in

turn require conformance checks, either automated or human, even if the photographic design is perfect. Commercial

quality-conformance checking tools are available, and recently have become subject to independent testing [9,10]. Human5 The ISO/IEC 19794-5:2005 Face Image standard [11], was amended in 2007 to guide photography [6] and, in 2009, to add 3D data. These additions and

and other improvements resulted in a second edition in 2011 [26]. Passport guidelines(e.g. [24]) are derived from this standard.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 16

mediated checks on image quality are common in passport and visa issuance procedures where photographers and, later,

immigration officials, check adherence to published quality requirements.

Results for the webcam partition have some applicability to cases where image quality is significantly degraded e.g. bank

ATM machine, surveillance, although the data is acquired from (mostly) cooperating subjects.

4 Performance metrics

This section gives specific definitions for accuracy and timing metrics. Tests of open-set biometric algorithms must quantify frequency of two error conditions:

. False alarms: Type I errors occur when search data from a person who has never been seen before is incorrectly

associated with one or more enrollees’ data.

. Misses: Type II errors arise when a search of an enrolled person’s biometric does not return the correct identity.

Many practitioners prefer to talk about “hit rates” instead of “miss rates” - the first is simply one minus the other as

detailed below. Sections 4.1 and 4.2 define metrics for the Type I and Type II performance variables.

Additionally, because recognition algorithms sometimes fail to produce a template from an image, or fail to execute a oneto-many search, the occurrence of such events must be recorded. Further because algorithms might elect to not produce a

template from, for example, a poor quality image, these failure rates must be combined with the recognition error rates to

support algorithm comparison. This is addressed in section 4.4.

Finally, section 4.5 discusses measurement of computation duration, and section 4.6 addresses the uncertainty associated

with various measurements. Template size measurement is included with the results.

4.1 Quantifying false alarms

It is typical for a search to be conducted into an enrolled population of N identities, and for the algorithm to be configured

to return the closest L candidate identities. These candidates are ranked by their score, in descending order. A human

analyst might examine either all L candidates, or just the top R ≤ L identities, or only those with score greater than

threshold, T. The workload associated with such examination is discussed later, in 5.7.

False alarm performance is quantified in two related ways. These express how many searches produces false positives,

and then, how many false positives are produced in a search.

False positive identification rate: The first quantity, FPIR, is the proportion of nonmate searches that produce an adverse

outcome:

FPIR(N, T, L) = Num. nonmate searches where one or more enrolled candidates are returned at or above threshold, T

Num. nonmate searches attempted.

(1)

Under this definition, FPIR can be computed from the highest nonmate candidate produced in a search - it is not necessary

to consider candidates at rank 2 and above. FPIR is the primary measure of Type I errors in this report.

Selectivity: However, note that in any given search, more than one nonmate may be returned above threshold. In order

to quantify such events, a second quantity, selectivity (SEL), is defined as the number of nonmates returned on a candidate

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 17

False Positive Identification Rate (FPIR), N=1600000

Selectivity

0.00005

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

A31C B32C

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

C31C

D31C E30C J33C

lifetime.mugshot recent.mugshot lifetime.webcam recent.webcam

Figure 5: Relationship of SEL and FPIR. For six algorithms from the six most accurate suppliers, the figures plot SEL(N, T, L) vs.

FPIR(N, T, L) parametrically with threshold, T. Selectivity is always greater than or equal to FPIR. The two are not equal when false

positives are concentrated in candidate lists rather than being distributed across searches. The population size is N = 1, 600, 000.

Algorithms were tasked with reporting L = 50 candidates. Each panel corresponds to one algorithm. The red lines correspond to

searching poor quality webcam images. The blue lines correspond to searching better quality mugshot images. Analogous plots for

other algorithms appear in the “report cards” of Appendix A.

list, averaged over all searches.

SEL(N, T, L) = Num. nonmate enrolled candidates returned at or above threshold, T

Num. nonmate searches attempted. (2)

Both of these metrics are useful operationally. FPIR is useful for targeting how often an adverse false positive outcome can

occur, while SEL as a number is related to workload associated with adjudicating candidate lists. The relationship between

the two quantities is complicated - it depends on whether an algorithm concentrates the false alarms in the results of a

few searches or whether it disburses them across many. Figure 5 plots SEL(T) vs. FPIR(T) for several algorithms. Note

that SEL≥FPIR, by definition. It is clear that some algorithms exhibit an identity relationship, SEL = FPIR, except at high

values, but that some algorithms give selectivity values with complicated dependence on FPIR. It is not clear that the

linear behavior is more, or less, desirable. On the one hand, the concentration of false matches in particular searches

would lead to reduced adjudication labor in the long run. But on the other hand, the presence of several false matches on

a candidate list is peculiar in that it is unlikely that several individuals would be biometrically similar to a search image.

Such occurrences must be due to some property common to the pair of the images (for example, similar pose, the presence

of moles, or heavy-framed glasses) and of algorithm sensitivity to it.

4.2 Quantifying hits and misses

If L candidates are returned in a search, a shorter candidate list can be prepared by taking the top R ≤ L candidates for

which T ≥ 0. This reduction of the candidate list is done because thresholds may be applied, and only short lists might be

reviewed (according to policy or labor availability, for example). It is useful then to state accuracy in terms of R and T, so

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 18

we define a “miss rate” with the general name false negative identification rate (FNIR), as follows:

FNIR(N, R, T, L) = Num. mate searches with enrolled mate found outside top R ranks or score below threshold, T

Num. mate searches attempted. (3)

This formulation is simple for evaluation in that it does not distinguish between causes of misses. Thus a mate that is not

reported on a candidate list is treated the the same as a miss arising from face finding failure, algorithm intolerance of

poor quality, or software crashes. Thus if the algorithm fails to produce a candidate list, either because the search failed,

or because a search template was not made, the result is regarded as miss, adding to FNIR.

Hit rates, and true positive identification rates: While FNIR states the “miss rate” as how often the correct candidate is either

not above threshold or not at good rank, many communities prefer to talk of “hit rates”. This is simply the true positive

identification rate(TPIR) which is the complement of FNIR giving a positive statement of how often mated searches are

successful:

TPIR(N, R, T, L) = 1 − FNIR(N, R, T, L) (4)

Reliability and sensitivity are corresponding terms, typically being identical to TPIR. This quantity is often cited in

automated fingerprint identification system (AFIS) evaluations.

An important special case is the cumulative match characteristic(CMC) which summarizes accuracy of mated-searches

only. It ignores similarity scores by relaxing the threshold requirement, and just reports the fraction of mated searches

returning the mate at rank R or better.

CMC(N, R, L) = 1 − FNIR(N, R, 0, L) (5)

We primarily cite the complement of this quantity 1−FNIR, the fraction of mates not in the top R ranks.

The rank one hit rate is the fraction of mated searches yielding the correct candidate at best rank, i.e. CMC(N, 1, L). While

this quantity is the most common summary indicator of an algorithms’ efficacy, it is not dependent on similarity scores, so

it does not distinguish between strong (high scoring) and weak hits. It also ignores that an adjudicating reviewer is often

willing to look at many candidates.

4.3 Best practice testing requires execution of searches with and without mates

FRVT embedded 1:N searches of two kinds: Those for which there is an enrolled mate, and those for which there is not.

The respective numbers for these types of searches appear in Table 3. However, it is common to conduct only mated

searches. This is bad practice because if the information that a mate always exists is revealed to a test participant, or can

be reasonably assumed, then unrealistic gaming of the test is possible. The cumulative match characteristic is computed

from candidate lists produced in mated searches. Even if the CMC is the only metric of interest, the actual trials executed

in a test should nevertheless include searches for which no mate exists. As detailed in Table 3 the FRVT reserved disjoint

populations of subjects for executing true nonmate searches.

4.4 Failure to extract

Template generation can fail either during enrolment, or ahead of one-to-many search. This report quantifies only failureto-enrol. This quantity is denoted FTE. It is computed as the proportion of images that do not produce an output template.

The corresponding search-phase number, FTX, is not reported because we consider the FTE number adequate. This is

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 19

supported by a) the observed error rates being very low, usually zero; b) we assume, that the same underlying algorithm

is used6; and c) the enrolment sets are much larger.

Failure to extract rates are incorporated into FNIR and FPIR measurements as follows.

. Enrolment templates: Any failed enrolment is regarded as producing a zero length template. Algorithms are required by the API [14] to transparently process zero length templates. The effect of template generation failure on

search accuracy depends on whether subsequent searches are mated, or nonmated: Mated searches will fail giving

elevated FNIR; Nonmated searches will not produce false positives, so FPIR will be reduced by (to first order) a

factor of 1−FTE.

. Search templates and 1:N search: In cases where the algorithm fails to produce a search template from input imagery, the result is taken to be a candidate list whose entries have no hypothesized identities and zero score. The

effect of template generation failure on search accuracy depends on whether searches are mated, or nonmated:

Mated searches will fail giving elevated FNIR; Nonmated searches will not produce false positives, so FPIR will be

reduced.

FNIR† = FTX + (1 − FTX)FNIR (6)

FPIR† = (1 − FTX)FPIR (7)

This approach is the correct treatment for positive-identification applications such as access control where cooperative

users are enrolled and make attempts at recognition. This approach is not appropriate to negative identification applications, such as visa fraud detection, in which hostile individuals may attempt to evade detection by submitting poor quality

samples. In those cases, template generation failure should be investigated as though a false alarm had occurred.

4.5 Timing measurement

Algorithms were submitted to NIST as implementations of the application programming interface(API) specified by NIST

in the Evaluation Plan [14]. The API includes functions for initialization, template generation, finalization, and search.

Two template generation functions are required, one for the preparation of an enrolment template, and one for a search

template.

In NIST’s test harness, all functions were wrapped by calls to the function get time of day() which enables duration measurements with microsecond resolution. Timing was measured on a dedicated computer equipped with 192GB of main

memory. The computer was not running any other processes except those back-grounded as part of the operating system.

Timing measurements do not include disk access unless the algorithm under test elected to access enrolment or configuration data during a search (something that is not necessary because the API supported initialization prior to searching).

The FRVT test plan formally stated the durations limits on the core elemental functions of the algorithms. The times were

stated as 90-th percentiles.

4.6 Uncertainty estimation

This study leverages operational datasets for measurement of recognition error rates. This affords several advantages.

First, large numbers of searches are conducted (see Table 3) giving precision to the measurements. Moreover, these do

6While this assumption is violated for at least Morpho’s algorithms which use larger search templates than enrolment templates, the failure to make a

template rates are very close to zero.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 20

not involve reuse of individuals, and thus, binomial statistics can be expected to apply to recognition error counts. In

that case, an observed count of a particular recognition outcome (i.e. a false negative or false positive) in M trials will

sustain 95% confidence that the actual error rate is no larger than some value. As an example, the minimum number of

searches conducted in this report is M = 10, 660, and the observed FNIR is never below 0.01 so the measurement supports

a conclusion that the actual FNIR is no higher than 0.0113 at 95% confidence level, and 0.0133 at the 99.9% level. On the

false positive side, we tabulate FNIR at FPIR values as low as 0.002. Given estimates based on 171,066 nonmate trials,

the actual FPIR values will be below 0.00214 (at 95%) and 0.00235 (at 99.9% confidence). The point is that large scale

evaluation, without reuse of subjects, supports tight uncertainty bounds on the measured error rates.

Bootstrapping is an empirical method of measuring the variability of a statistic, often employed when the variability

cannot be determined analytically. In the context of this evaluation, bootstrapping is sometimes used to measure the

distribution of error statistics (i.e. FNIR or FPIR) at a fixed threshold. Each bootstrap iteration samples with replacement

from the original set of comparisons. The statistic of interest is then computed over the sampled data. This process is

repeated for a large number of bootstrap iterations to produce a distribution of the measured statistic. Bootstrapping

relies on several assumptions, including that the sample data is independent and identically distributed. However, when

different comparisons involve the same individual, the comparisons are likely to be correlated due to the existence of

Doddington’s zoo [7]. Thus, the independence assumption is violated. Determining the effect this has on the bootstrapped

distributions is beyond the scope of this evaluation, but the likely result is an underestimation of the variability of FNIR

and/or FPIR in some cases. The experimental design here avoids this issue.

5 Results

This section details performance of the algorithms submitted to the Class-C identification algorithm track of the FRVT

evaluation. Performance metrics were described in section 4. The following subsections address specific aspects of performance. Appendix A includes a report for each algorithm, where multiple plots are assembled side-by-side.

5.1 Comparative accuracy

Methods: LEO/Mughots and LEO/Webcam images are enrolled in their natural distributions using both the “lifetime”

and “recent” enrolment types of section 3.2. Four different population sizes are used. The numbers of enrolled individuals

are 20000, 160000, 640000, and 1600000. For the recent enrolment type, the number of images is identical to the number of

individuals. For the lifetime enrolment type, the numbers of enrolled images are: 32948, 225585, 856934, and 2117750 as

documented in Table 3. Each successive enrolled population includes the subjects and images of the smaller population.

Two nonmate search sets are used, one containing 10660 webcam images, and another containing 171066 mugshots.

Results: Table 4 presents FNIR values for all algorithms submitted to FRVT in 20137 applied to an enrolled population size

of N = 160,000. Table 5 presents the same data for N = 640,000 but it excludes algorithms that did not demonstrate high

accuracy in the N = 160K trial. Table 6 likewise presents the same data for N = 1.6 million enrolled identities.

Figure 6 shows detection error tradeoff (DET) characteristics for algorithms from the most capable algorithms. These

summarize the tradeoff of FNIR for FPIR for searches conducted into enrolled galleries containing 1.6 million people.

Discussion: As in the 2010 test, the algorithms from the NEC corporation give broadly the lowest error rates on all

7The table excludes entries that did not execute to completion or that did not meet minimum speed criteria. It additionally excludes algorithms submitted

to phase 1 of the FRVT in August 2012.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 21 ENROL ALL LIFETIME PHOTOS MOST RECENT PHOTO TEMPLATE SEARCH

N=160K MUGSHOTS BOTH MUGSHOTS WEBCAM SIZE (BYTES) TIME (MSEC)ALG FPIR=0.002 FPIR=0.02 R=50 R=10 R=1 FTE FPIR=0.02 R=1 WORK FPIR=0.02 R=1 WORK ENROL SEARCH TEMPLATE SEARCH

A20C 230.247 0.193 220.063 0.083 220.127 0.000 0.207 0.145 4.575 0.496 170.355 10.975 4106 4106 30522 311235A30C 210.240 0.185 170.055 0.074 200.116 0.000 0.199 0.133 4.021 0.426 160.291 8.281 4622 4622 19315 16243A31C 220.241 0.183 180.055 0.074 190.116 0.000 0.198 0.133 4.021 0.425 150.291 8.281 4622 4622 21328 17245A32C 250.261 0.200 230.068 0.087 240.132 0.000 0.247 0.173 5.501 0.536 250.394 12.574 1046 1046

8194

5102

B30C 140.183 0.144 190.058 0.072 170.100 0.002 0.171 0.120 4.016 0.840 370.604 21.956 4796 4796 18295 23756B31C 160.191 0.143 200.058 0.072 160.100 0.002 0.170 0.120 4.016 0.854 360.604 21.955 4796 4796 17280 24760B32C 170.193 0.137 160.050 0.063 150.091 0.002 0.164 0.109 3.508 0.795 300.502 16.961 9932 9932 27441 372021B33C 190.204 0.139 150.050 0.063 140.091 0.002 0.165 0.109 3.508 0.792 290.502 16.961 9932 9932 25386 382022C20C 290.543 0.281 330.115 0.139 280.177 0.000 0.319 0.208 7.651 0.906 430.784 33.648 35994 35994 14225 301108C30C 310.566 0.267 250.082 0.106 260.146 0.000 0.304 0.173 5.683 0.817 350.603 21.964 37008 37008 24366 427117C31C 270.518 0.254 260.084 0.106 250.143 0.000 0.291 0.169 5.762 0.827 390.640 25.118 37008 37008 23361 321256C32C 300.558 0.276 270.087 0.113 270.155 0.000 0.314 0.183 6.017 0.819 380.612 22.490 5040 37008 22360 291004D20C

90.128 0.095 130.044 0.050 100.068 0.000 0.114 0.081 2.856 0.369 100.252 7.728 8005 20489 42713 22510

D30C

80.122 0.095 110.042 0.048

80.067 0.000 0.109 0.078 2.716 0.369 110.254 7.688 8562 21046 40709 26806

D31C

70.122 0.091 100.042 0.048

50.065 0.000 0.107 0.077 2.691 0.379 140.260 7.754 12247 24731 38708 331275

D32C 200.236 0.182 240.076 0.094 230.128 0.000 0.207 0.150 5.125 0.512 240.389 13.751 857 857 28469

387

D33C 110.144 0.100 120.042 0.048

70.066 0.000 0.106 0.076 2.706 0.374 120.258 7.802 8005 20489 41710 27809

D34C 100.142 0.094

70.038 0.046

60.066 0.000 0.106 0.076 2.571 0.379 130.259 7.659 8005 20489 39708 25805

E20C

30.066 0.048

20.018 0.023

40.032 0.000 0.057 0.037 1.202 0.145

20.081 2.088 2465 2465 10205

8120

E21C

40.073 0.052

10.018 0.023

30.032 0.000 0.060 0.037 1.202 0.169

10.081 2.088 2465 2465

9204 10139

E30C

10.052 0.040

30.019 0.022

10.029 0.000 0.047 0.034 1.179 0.136

40.084 2.196 2529 2529 11205

7118

E31C

20.054 0.042

40.021 0.023

20.031 0.000 0.047 0.035 1.289 0.135

30.082 2.397 2529 2529 13213

152

F20C 330.656 0.494 350.155 0.202 350.282 0.006 0.538 0.312 10.181 0.713 280.474 15.197 6760 6760

2100 18341

F30C 380.749 0.459 290.108 0.143 300.214 0.001 0.490 0.247 7.650 0.639 180.373 11.338 6440 6440 15257 351525F31C 390.791 0.460 310.108 0.142 320.214 0.002 0.493 0.248 7.615 0.643 230.382 11.451 7484 7484 16265 361940G30C 370.709 0.502 340.119 0.162 340.245 0.010 0.538 0.285 8.439 0.718 260.444 12.736 3484 3484

3102 28811

G31C 280.536 0.429 320.113 0.150 330.219 0.010 0.477 0.254 7.898 0.602 210.382 11.830 2240 2240

5170

284

H30C 320.614 0.523 360.183 0.229 360.309 0.000 0.566 0.353 12.221 0.705 270.468 15.692 4420 4420

143 14233

J20C 240.254 0.176 140.044 0.063 180.102 0.000 0.216 0.121 3.341 0.433

90.250 6.933 6206 6206 29490 11148

J30C 150.189 0.129

80.039 0.051 120.079 0.000 0.150 0.091 2.628 0.339

70.203 5.407 4158 4158 31526

6110

J31C 180.197 0.135

90.039 0.053 130.082 0.000 0.156 0.094 2.655 0.347

80.207 5.444 4158 4158 32530

9130

J32C 130.179 0.117

60.034 0.044 110.069 0.000 0.138 0.081 2.335 0.314

60.184 4.639 8254 8254 34545 15236

J33C 120.171 0.114

50.033 0.043

90.067 0.000 0.133 0.079 2.255 0.305

50.181 4.516 8254 8254 35548 13188

L30C 410.850 0.712 430.293 0.351 420.442 0.003 0.743 0.490 18.525 0.932 420.708 27.775 7328 7328 33539 414031L31C 441.000 0.639 410.253 0.310 410.398 0.003 0.679 0.444 16.272 0.903 400.658 25.050 7328 7328 37635 404000M20C 350.678 0.592 400.221 0.278 400.371 0.024 0.627 0.412 14.088 0.770 340.587 21.009 5608 5608 43 43M21C 360.678 0.586 390.211 0.265 380.355 0.024 0.618 0.390 13.282 0.753 320.563 19.754 5608 5608 44 44M30C 340.667 0.576 370.198 0.252 370.342 0.024 0.618 0.390 13.279 0.753 310.562 19.768 5608 5608

4104 12171

P30C 260.421 0.291 210.059 0.080 210.123 0.005 0.293 0.151 4.664 0.601 190.375 11.687 3713 3713 36559 392930Q30C 430.915 0.595 380.206 0.265 390.361 0.004 0.632 0.405 13.897 0.770 330.567 18.766 5600 5600

7191 341390

S20C 420.851 0.773 440.373 0.433 440.523 0.033 0.800 0.569 22.745 0.928 440.810 34.726 800 800

6171 20444

T30C 400.836 0.753 420.288 0.350 430.449 0.000 0.779 0.495 18.360 0.838 410.664 25.564 1936 1936 12207

494

Table 4: Accuracy, speed, template sizes at N = 160K: For LEO mugshots and webcam images, the values are FNIR “miss rates” except at right where template sizes and

search durations are shown, and the column FTE indicating rate of template generation failure. Five different accuracy criteria appear: At left are values for high-threshold,

low false positive rates relevant to more “lights-out” applications with minimal human adjudication. The next columns apply to no-threshold rank-based criteria relevant toinvestigative applications where a reviewer is permanently employed to adjudicate candidate lists of length 50, 10 or 1. The column “WORK” gives the workload statistic ofeq. 13, with recidivism rate 100% (β = 1) gives the expected number of candidate reviews a reviewer must make. From left to right, the colored columns are likely the mostimportant metrics for, respectively, the “lights-out” , the ”investigational” and the “poor image” communities. The blue superscripts give column-wise algorithm rankings.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 22 ENROL ALL LIFETIME PHOTOS MOST RECENT PHOTO TEMPLATE SEARCH

N=640K MUGSHOTS BOTH MUGSHOTS WEBCAM SIZE (BYTES) TIME (MSEC)ALG FPIR=0.002 FPIR=0.02 R=50 R=10 R=1 FTE FPIR=0.02 R=1 WORK FPIR=0.02 R=1 WORK ENROL SEARCH TEMPLATE SEARCH

A20C 210.274 0.219 220.080 0.104 230.150 0.000 0.231 0.167 5.579 0.534 170.396 13.237 4106 4106 26524 304921A30C 200.267 0.212 190.071 0.092 190.139 0.000 0.225 0.156 4.963 0.466 150.333 10.329 4622 4622 17344 20979A31C 190.267 0.211 200.071 0.092 200.139 0.000 0.223 0.156 4.963 0.465 160.334 10.328 4622 4622 16335 16956A32C 220.288 0.230 230.084 0.107 240.156 0.000 0.272 0.200 6.641 0.576 210.439 15.117 1046 1046

6188

5419

B30C 110.208 0.161 180.069 0.085 170.113 0.002 0.190 0.136 4.770 0.872 310.649 25.371 4796 4796 15284 222975B31C 130.222 0.163 170.069 0.085 160.113 0.002 0.192 0.137 4.770 0.886 300.649 25.372 4796 4796 14280 232985B32C 150.231 0.156 160.061 0.075 150.104 0.002 0.186 0.125 4.232 0.840 250.546 20.215 9932 9932 22381 328068B33C 170.248 0.160 150.061 0.075 140.104 0.002 0.189 0.125 4.232 0.836 240.546 20.215 9932 9932 21378 318058C20C 260.617 0.307 280.134 0.157 280.194 0.000 0.348 0.229 8.763 0.921 370.807 35.913 35994 35994 11240 284361C30C 36 250.102 0.126 260.165 0.000 0.195 6.879 290.643 24.988 37008 37008 20373 3428399C31C 250.605 0.283 260.103 0.124 250.162 0.000 0.323 0.191 6.865 0.855 330.676 27.597 37008 37008 18362 294766C32C 280.663 0.310 270.109 0.133 270.175 0.000 0.351 0.207 7.293 0.853 320.654 25.540 5040 37008 19363 274052D20C

70.131 0.100 130.053 0.058

90.075 0.000 0.119 0.089 3.378 0.391

90.278 9.527 8005 20489 44 44

D30C

60.129 0.101 120.051 0.056

80.073 0.000 0.116 0.087 3.233 0.397 100.282 9.528 8562 21046 31705 17956

D31C

50.127 0.096 100.051 0.055

50.071 0.000 0.115 0.084 3.185 0.408 140.287 9.597 12247 24731 33709 211425

D32C 180.260 0.202 240.090 0.109 210.144 0.000 0.229 0.168 6.033 0.550 190.430 15.765 857 857 23472

2260

D33C

90.156 0.106 110.051 0.056

60.072 0.000 0.112 0.085 3.228 0.398 110.284 9.632 8005 20489 34711 19965

D34C

80.153 0.101

70.046 0.053

70.073 0.000 0.114 0.086 3.037 0.406 120.286 9.147 8005 20489 32708 18958

E20C

30.076 0.057

30.022 0.027

40.038 0.000 0.067 0.044 1.438 0.173

30.100 2.702 2465 2465 10216

8522

E21C

40.090 0.062

20.022 0.027

30.038 0.000 0.074 0.044 1.438 0.204

20.100 2.702 2465 2465

7205

7475

E30C

10.059 0.044

10.021 0.024

10.032 0.000 0.052 0.037 1.301 0.160

40.100 2.766 2529 2529

8207

6458

E31C

20.062 0.046

40.024 0.026

20.034 0.000 0.053 0.038 1.461 0.157

10.098 3.033 2529 2529

9208

1168

J20C 230.294 0.205 140.058 0.078 180.119 0.000 0.248 0.143 4.244 0.498 130.287 8.729 6206 6206 24482 11572J30C 140.227 0.154

80.050 0.063 120.093 0.000 0.179 0.109 3.332 0.389

70.241 7.179 4158 4158 27530 10555

J31C 160.238 0.165

90.050 0.065 130.099 0.000 0.187 0.113 3.369 0.400

80.247 7.252 4158 4158 25523

9554

J32C 120.218 0.141

60.044 0.055 110.083 0.000 0.164 0.097 2.980 0.359

60.219 6.259 8254 8254 28541 13766

J33C 100.208 0.135

50.042 0.053 100.081 0.000 0.159 0.095 2.863 0.352

50.214 6.042 8254 8254 29552 14812

P30C 240.470 0.344 210.076 0.100 220.147 0.005 0.332 0.172 5.784 0.644 180.421 14.182 3713 3713 30599 3311620

Table 5: Accuracy, speed, template sizes at N = 640K: For LEO mugshots and webcam images, the values are FNIR “miss rates” except at right where template sizes and

search durations are shown, and the column FTE indicating rate of template generation failure. Five different accuracy criteria appear: At left are values for high-threshold,

low false positive rates relevant to more “lights-out” applications with minimal human adjudication. The next columns apply to no-threshold rank-based criteria relevant toinvestigative applications where a reviewer is permanently employed to adjudicate candidate lists of length 1, 10 or 50. The workload statistic (equation 13), with recidivismrate 100% (β = 1) gives the expected number of candidate reviews a reviewer must make. From left to right, the colored columns are likely the most important metrics for,respectively, the “lights-out” , the ”investigational” and the “poor image” communities. The blue superscripts give column-wise algorithm rankings.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 23 ENROL ALL LIFETIME PHOTOS MOST RECENT PHOTO TEMPLATE SEARCH

N=1600K MUGSHOTS BOTH MUGSHOTS WEBCAM SIZE (BYTES) TIME (MSEC)ALG FPIR=0.002 FPIR=0.02 R=50 R=10 R=1 FTE FPIR=0.02 R=1 WORK FPIR=0.02 R=1 WORK ENROL SEARCH TEMPLATE SEARCH

A20C 210.294 0.236 210.093 0.119 220.166 0.000 0.247 0.182 6.285 0.559 170.427 14.965 4106 4106 18511 2212325A30C 200.288 0.229 190.083 0.107 210.155 0.000 0.240 0.172 5.643 0.498 150.364 11.870 4622 4622 10316 172458A31C 190.287 0.228 200.083 0.107 200.155 0.000 0.239 0.172 5.643 0.497 160.364 11.870 4622 4622

9314 162454

A32C 220.312 0.248 220.096 0.122 230.172 0.000 0.291 0.216 7.489 0.604 190.474 16.920 1046 1046

1184

31067

B30C 100.231 0.173 180.078 0.094 170.123 0.002 0.205 0.147 5.291 0.891 240.673 27.447 4796 4796

8274 187408

B31C 120.249 0.175 170.078 0.094 160.123 0.002 0.207 0.147 5.291 0.903 230.673 27.446 4796 4796

7274 197469

B32C 150.268 0.170 160.069 0.084 150.113 0.002 0.201 0.136 4.741 0.865 200.576 22.235 9932 9932 15378 2520367B33C 180.286 0.174 150.069 0.084 140.113 0.002 0.206 0.136 4.748 0.861 210.576 22.244 9932 9932 14377 2420313C20C 250.655 0.325 270.147 0.169 270.206 0.000 0.367 0.243 9.475 0.928 270.823 37.275 35994 35994

6217 2110815

C30C 35 250.115 0.139 250.179 0.000 0.211 7.662 220.669 27.038 37008 13361 2671794C31C 240.653 0.303 240.114 0.135 240.173 0.000 0.344 0.205 7.567 0.872 260.702 29.369 37008 37008 12361 2312414C32C 260.705 0.333 260.122 0.146 260.190 0.000 0.375 0.223 8.097 0.876 250.682 27.611 5040 37008 11358 2010067D20C

70.137 0.104 130.061 0.066

90.080 0.000 0.124 0.096 3.792 0.408

90.298 10.839 8005 20489 44 44

D30C

60.136 0.107 120.059 0.063

80.080 0.000 0.123 0.093 3.678 0.418 100.302 10.777 8562 21046 23706

91213

D31C

50.134 0.102 100.059 0.063

50.077 0.000 0.120 0.091 3.620 0.427 130.307 10.913 12247 24731 26713 131756

D32C 170.283 0.219 230.101 0.120 190.155 0.000 0.246 0.180 6.647 0.582 180.462 17.177 857 857 17470

2607

D33C

80.161 0.111 110.059 0.063

60.078 0.000 0.119 0.091 3.673 0.418 110.303 10.877 8005 20489 25713 111271

D34C

90.167 0.107

70.054 0.059

70.079 0.001 0.121 0.092 3.429 0.425 120.307 10.206 8005 20489 24706 101225

E20C

30.089 0.064

30.025 0.030

40.042 0.000 0.075 0.049 1.603 0.192

40.115 3.198 2465 2465

2196

71144

E21C

40.105 0.071

20.025 0.030

30.042 0.000 0.084 0.049 1.603 0.229

30.115 3.198 2465 2465

3200 121278

E30C

10.068 0.049

10.023 0.026

10.035 0.000 0.057 0.041 1.445 0.176

20.113 3.201 2529 2529

5206

41083

E31C

20.070 0.050

40.026 0.029

20.037 0.000 0.058 0.042 1.602 0.176

10.113 3.520 2529 2529

4201

1257

J20C 230.339 0.227 140.068 0.090 180.133 0.000 0.275 0.161 4.940 0.534 140.318 10.162 6206 6206 16466

51112

J30C 140.258 0.173

80.058 0.071 120.104 0.000 0.200 0.122 3.831 0.422

70.270 8.371 4158 4158 20525

61118

J31C 160.275 0.184

90.058 0.074 130.110 0.000 0.208 0.127 3.886 0.432

80.274 8.452 4158 4158 19523

81195

J32C 130.256 0.158

60.051 0.063 110.093 0.000 0.185 0.108 3.448 0.394

60.246 7.427 8254 8254 22546 152450

J33C 110.237 0.153

50.049 0.061 100.091 0.000 0.179 0.107 3.332 0.382

50.237 7.142 8254 8254 21541 142100

P30C 270.731 0.652 280.530 0.540 280.564 50.000 33 50.000 32 32

Table 6: Accuracy, speed, template sizes at N = 1.6M: For LEO mugshots and webcam images, the values are FNIR “miss rates” except at right where template sizes and

search durations are shown, and the column FTE indicating rate of template generation failure. Five different accuracy criteria appear: At left are values for high-threshold,low false positive rates relevant to more “lights-out” applications with minimal human adjudication. The next columns apply to no-threshold rank-based criteria relevant toinvestigative applications where a reviewer is permanently employed to adjudicate candidate lists of length 1, 10 or 50. The workload statistic (equation 13), with recidivismrate 100% (β = 1) gives the expected number of candidate reviews a reviewer must make. From left to right, the colored columns are likely the most important metrics for,respectively, the “lights-out” , the ”investigational” and the “poor image” communities. The blue superscripts give column-wise algorithm rankings.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 24

False Positive Identification Rate (FPIR), N=1600000

False Negative Identification Rate (FNIR)

0.05

 0.1

 0.2

 0.5

0.0004 0.001 0.004 0.01 0.04 0.1 0.4

●●

●●

 3212 ●

●

●●

 3145 ●

●

●

 3086

A31C

●

●

●●

0.3834

●

●

●●

0.364

●

●

●●

0.3554

B32C

0.0004 0.001 0.004 0.01 0.04 0.1 0.4

●●

●●

23140

●

●

●●

21854

●

●

●●

21546

C31C

●

●

●●

 4525

●

●

●

●

 4005 ●

●

●

●

 3709

D31C

●

●

●●

0.5323

●

●

●

●

0.5198 ●

●

●

●

0.5118

E30C

●

●

●●

0.5037

●

●

● ●

0.4788

●

●

● ●

0.4651

J33C

lifetime.mugshot recent.mugshot lifetime.webcam recent.webcam

Figure 6: Identification error tradeoff for most accurate developers. For mugshot and webcam images searched against images of 1.6

million individuals enrolled in the “recent” and “lifetime” types of section 3.2, each panel plots FNIR (miss rate) vs. FPIR (false alarm

rate) on log-log axes. Each panel presents data for the leading algorithm from the providers of the six most accurate algorithms. The

(upper) red lines correspond to searching poor quality webcam images. The (lower) blue lines correspond to searching better quality

mugshot images. The short grey lines link points of fixed threshold; they show how the error rates change with image and enrolment

types. The threshold iitsel is shown in dark red. Non vertical lines indicate FPIR variation that a system operator would need to plan

for. Performance data is tabulated in Tables 10 and 4- 6. DETs for other algorithms appear in the “report cards” of Appendix A.

datasets. This applies across image types, mugshots and webcam, and enrolment types (sec: 3.2). FNIR values for NEC

are a factor of two or more lower than for the closest competitors, Morpho and Toshiba. For example, when 1.6 million

individuals’ lifetime mugshots are enrolled, the NEC’s rank one miss rate is 0.035 vs. Morpho’s 0.077. At rank 50, the NEC

result is 0.023 with Toshiba at 0.049 and Morpho at 0.054. Similarly the margin for webcam images is larger still: Table

6 shows rank one miss rate for NEC is 0.11 vs. 0.24 for Toshiba and 0.30 for Morpho. Indeed the NEC algorithms yield

webcam recognition accuracy better than many algorithms’ accuracy on mugshot images.

At the other end of the DET plot, however, the NEC algorithm exhibits rapid elevation in error rates at FPIR < 0.001. There

the lifetime mugshot DETs of Figure 6 show that Morpho’s algorithm gains a modest advantage: at FPIR = 0.0003, the NEC

miss rate is 0.374 while the Morpho value is 0.311. While the shape of the DETs is persuasive, this result is at the limits of

statistical significant since the number of false positives, 51, supports a statement only that FPIR is, with 99.9% confidence,

below 0.00044 and there, Morpho’s error rate is virtually tied with NECs: 0.248 vs. 0.252 respectively. Refinement of this

comparison would require more nonmate searches to improve FPIR estimates.

For mugshots, Toshiba’s algorithms give slightly fewer misses than Cognitec’s which, in turn, produce error rates below

those of 3M/Cogent. Finally Neurotechnology’s algorithms are beaten by those of Zhuhai Yisheng at N < 640, 000 but

not for N = 1, 600, 000 where the P30C algorithm fails substantially.

All algorithms are intolerant of webcam images, giving elevated miss rates, a factor of around three higher than for

mugshots. This is particularly true for algorithms from Neurotechnology which give miss rates in excess of 0.6. As

discussed earlier, results for webcam images have only niche operational relevance because most contemporary systems

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 25

ENROLLED RANK ONE ACCURACY

N=1600000 SDK YYYY-MM FNIR(R=1) FNIR(R=10) FNIR(R=50) FNIR(FPIR=0.002)

Cognitec X21 2010-02 0.187 0.148 0.125 0.342

B10C 2012-08 0.213 0.174 0.151 0.349

B32C 2013-10 0.170 0.140 0.121 0.421

Neurotechnology Z22 2010-02 0.258 0.219 0.194 0.537

C11C 2012-08 0.268 0.232 0.208 0.952

C20C 2013-03 0.267 0.229 0.206 0.684

C31C 2013-10 0.231 0.193 0.170 0.685

Safran Morpho W22 2010-02 0.135 0.113 0.103 0.248

D10C 2012-08 0.145 0.126 0.120 0.233

D20C 2013-03 0.124 0.106 0.100 0.194

D31C 2013-10 0.121 0.103 0.099 0.195

NEC V21 2010-02 0.089 0.063 0.050 0.269

E10C 2012-08 0.164 0.121 0.098 0.410

E20C 2013-03 0.072 0.055 0.047 0.132

E30C 2013-10 0.064 0.051 0.046 0.108

Table 7: Accuracy gains and losses 2010 to 2013: For the four providers whose algorithms successfully completed the largest identification trial in the 2010 MBE evaluation [12], the table shows how accuracy has changed in the three and half years from early 2010 to

late 2013. This is done for a fixed dataset of N = 1.6 million individuals each of whom is enrolled with K ≥ 1 historical images. The

number of mated searches is 40,000. The number of nonmate searches is also 40,000. The images are a subset of the LEO dataset drawn

randomly in their natural mixture of approximately of 86% mugshots and 14% webcams.

target the ISO/IEC 19794-5 standard’s appearance requirements. That said the algorithm providers were aware that such

images had been used in the 2010 test, and have access to images of this type in the MEDS Special Database 32 [5].

5.1.1 Face accuracy 2013 vs. 2010

Methods: Algorithms from providers who participated in both 2010 and 2013 were used to identify LEO images, repeating

almost exactly the N = 1.6 million trial reported in MBE 2010 [12]. A small percentage of duplicate same-image pairs that

were removed from the 2010 results were not removed in this study. The number of candidates was restricted to L = 50.

Results: Table 7 shows modest reductions in miss rates from 2010 to 2013.

Discussion: At rank 1, there will be approximately 10% fewer misses for the most accurate Cognitec, Neurotechnology and

Morpho algorithms and nearly 30% fewer with NEC’s. At rank 50, the gains are more modest, 10% for Neurotechnology

and NEC, and below 4% for Cognitec and Morpho. At the other end of the DET curve, where thresholds are applied to

produce false positive outcomes in one in every 500 searches (FPIR = 0.002) the situation is mixed. While NEC realizes a

full 60% reduction in miss rate, and Morpho a 20% reduction, both Neurotechnology and Cognitec appear to have worse

accuracy. These observations ignore speed differences - particularly, the Cognitec algorithms in 2013 are considerably

faster than those evaluated in 2010.

5.1.2 Cross-acquisition recognition

The prior subsection gave exhaustive tabulations of results for the general LEO population. There, mated pairs could be

either mugshot-mugshot, webcam-webcam, or mugshot-webcam, and accuracy was only reported by whether the search

image was a mugshot or a webcam. This section reports specifically on the accuracy of the various combinations. The

experimental design is given in Table 8 and the results appear in Table 9.

For all the algorithms tested, the best recognition is obtained, as expected, by searching mugshots against enrolled

mugshots. The worst recognition results, however, are not obtained by searching webcams against webcams, but instead from searching webcams against mugshots. This result is unfortunately contrary to the aspiration of frontal image

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 26

Enrolment Search

Type Num. IDs Num. Images Num. Images and IDs

See Mate Nonmate

See sec. 3.2 N Webcam Mugshot Total Webcam Mugshot Webcam Mugshot

9 RECENT 0 10,660 0 10,660 10,660 0 28,936 171,066

10 RECENT 10,660 0 10,660 10,660 0 10,660 28,936 171,066

11 RECENT 10,660 0 10,660 10,660 10,660 0 28,936 171,066

Table 8: Enrolment and search sets. Each row summarizes one identification trial. The column labeled “Num. IDs” gives the number of

enrolled identities. This precedes the numbers of images, and then the number of mate, and nonmate, searches. Rows 9-11 are dedicated

to mugshot-mugshot, webcam-webcam, and mugshot-webcam recognition - the number of individuals here is limited to 10660 which

corresponds to the number of individuals who were captured with webcams and traditional mugshot cameras.

RANK 1 RANK 50

ENROL MUGSHOT MUGSHOT WEBCAM MUGSHOT MUGSHOT WEBCAM

SEARCH MUGSHOT WEBCAM WEBCAM MUGSHOT WEBCAM WEBCAM

A31C 50.092 30.467 40.264 50.042 40.187 40.107

B32C 40.082 60.591 80.476 40.040 70.344 80.240

C31C 70.125 90.782 100.617 70.063 90.472 110.428

D31C 30.061 40.489 30.230 30.036 30.184 30.105

E30C 1

0.025 10.104 10.066 10.018 10.031 10.026

G31C 8

0.182 12 50.343 80.075 12 50.153

H30C 90.271 80.762 70.428 90.136 80.389 70.223

J32C 20.052 20.236 20.153 20.023 20.067 20.051

L31C 110.358 100.913 110.629 110.188 100.714 100.389

M30C 100.298 7

0.670 90.521 100.142 60.341 90.280

P30C 60.107 50.530 60.344 60.045 50.242 60.161

S20C 120.492 110.968 120.789 120.306 110.890 120.604

Table 9: Mugshots, webcams, and interoperable recognition: The figures are miss rates, FNIR(N, R, T, L) with N = 10, 660, R =

{1, 50} , L = 50, T = 0, for enrolment using the image type given on row 2 and search image type given on row 3. The blue superscripts

indicate algorithm rankings, by column. Missing values are due to software crashes.

standards (i.e. ISO/IEC 19794-5), which aims to support best accuracy by establishing full-frontal geometry requirements

for all images. Here recognition accuracy is better if either both images are full-frontal (i.e. mugshots) or both are not (i.e.

webcams). As soon as there is heterogeneity (i.e. mugshot-webcam) recognition accuracy degrades. The specific cause

of this result is that while the webcam images are inferior, any given pair can share the same non-frontal pose (typically

an adverse pitch angle). Further study of this issue might quantify the relative roles of pose angle, optical resolution and

illumination.

5.2 Accuracy dependence on rank

Identification algorithms yield candidate lists where the mate is sometimes not at rank 1 because it has a low score relative

to some nonmates. This occurs typically because the face is of poor quality or has some difference to the enrolment image.

While rank-1 performance is a primary indicator for comparison of algorithms, a more relevant metric for applications

where a human reviewer is retained to adjudicate an L element candidate list, is the frequency at which mates can be

found at ranks up to rank L. In this report we quantify this as the miss rate FNIR(N, R, 0, L) for R → L, and we fix L = 50.

The workload associated with reviewer adjudication of candidate lists is discussed further in 5.7.

Method: The benefit of considering higher ranks is quantified by re-analyzing results from the same LEO images and

enrolment and search sets given in rows 1-8 of Table 3 i.e. for N up to 1.6 million identities.

Results: Previously, Tables 4 - 6 had given some FNIR values for, respectively, N = 160K, 640K and 1.6M. A visualization

of those results for N = 640K appears in Figure 8 which shows FNIR vs. R on log-log axes.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 27

Rank, 1 <= R <= 50, N = 10660

False Negative Identification Rate (FNIR)

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1 3 10 30 A31C

B32C

 1 3 10 30 C31C

D31C

 1 3 10 30 E30C

G31C

H30C

J32C

L31C

M30C

P30CS20C

mugshot_mugshot mugshot_webcam webcam_webcam

Figure 7: Identification accuracy by acquisition type. The figures plot miss rate vs. rank i.e FNIR(N, R, 0, L) with N = 10, 660 and

L = 50. Each panel corresponds to one algorithm. The three traces correspond to the use of the identified acquisition type for the

enrolment and search image respectively. Notably, cross-type recognition gives the worst accuracy.

Discussion: The following observations are notable.

. Magnitude of accuracy gains: By definition, miss rates decrease monotonically with R, and the size of the effect can

readily extend to a factor of two reduction in FNIR from R = 1 to R = 50.

. Differences across algorithms: For enrolment of N = 640K “lifetime” identities, the A31C algorithm gives a miss

rate of 0.071 at rank 50 vs. 0.139 at rank 1 for a miss rate reduction factor of 2.0. For B32C, the values are 0.061

vs. 0.104 for a gain of 1.7. For C31C, the values are 0.103 vs. 0.162 implying a gain of 1.6. For D31C, the factor is

1.4 from 0.051 vs. 0.071. For the most accurate algorithm overall E30C, FNIR reduces to 0.021 from 0.032 for a gain

of 1.5. Finally for J33C, the gain is 1.9 as FNIR dropped to 0.042 from 0.081. These trends are evident in Figure 8.

These ratios broadly apply to other algorithms from the respective suppliers. The more accurate algorithms typically

realize fewer gains at rank 50 because they place the mate at rank 1 more frequently. The result, however, is that the

J33C algorithm is inferior to the D-series algorithms at rank 1, but superior at rank 50.

. Power-law dependency: The observation that many of the plots in Figure 8 are straight lines is discussed later in

section 5.3.1. Note the plots for the D-series and F-series algorithms are not straight.

5.3 Effect of population size

How algorithmic performance degrades with increasing population size is a primary challenge in any application where

individuals are enrolled at a greater rate than they are un-enrolled. This scalability issue is examined in this section by

re-analyzing results from the same LEO images and enrolment and search sets given in rows 1-8 of Table 3 i.e. for N up to

1.6 million identities.

The results are presented in four ways as follows. This includes results for both mugshot and webcam searches, and both

recent and lifetime enrolment types.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 28

Rank, 1 <= R <= 50, N = 640000

False Negative Identification Rate (FNIR)

 0.2 0.1 1 3 10 30

A20C

 0.2 0.1

A30C

 0.2 0.1 1 3 10 30

A31C

 0.2 0.1

A32C

 0.5 0.2 0.1 1 3 10 30

B30C

 0.5 0.2 0.1

B31C

 0.5 0.2 0.1 1 3 10 30

B32C

 0.5 0.2 0.1

B33C

 0.5 0.2

C20C

 0.5 0.2 0.1

C30C

 0.5 0.2 0.1

C31C

 0.5 0.2 0.1

C32C

 0.2 0.1 0.05

D20C

 0.2 0.1 0.05

D30C

 0.2 0.1 0.05

D31C

 0.2 0.1

D32C

 0.2 0.1 0.05

D33C

 0.2 0.1 0.05

D34C

 0.1 0.05 0.02

E20C

 0.1 0.05 0.02

E21C

 0.1 0.05 0.02

E30C

 0.1 0.05

E31C F30C F31C

 0.5 0.2

G30C

 0.2

G31C

 0.5

H30C

 0.2 0.1

J20C

 0.2 0.1 0.05

J30C

 0.2 0.1 0.05

J31C

 0.2 0.1 0.05

J32C

 0.2 0.1 0.05

J33C

 0.5

M20C

 0.5

M21C

 0.5

M30C

 0.2 0.1

P30C

 0.5

T30C

lifetime.mugshot recent.mugshot lifetime.webcam recent.webcam Figure 8: Identification accuracy gains with human search for mates at poor rank. The figures plot miss rate vs. rank i.e FNIR(N, R, 0, L) with N = 640,000, and L = 50. Each panel corresponds to one algorithm. Note that because the vertical scales vary for each algorithm and the scale is logarithmic, the visually perceived gradients of the lines are not comparable (see Tables 4-6 for tabulated values). The (upper) red lines correspond to searching poor quality webcam images. The (lower) blue lines correspond to searching better quality mugshot images.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 29

Number of enrolled identities (thousands)

False Negative Identification Rate (FNIR), Rank = 1, threshold = 0

 0.5 0.2 0.1 0.05

 30 100 300 1000

A20C

 0.2 0.1 0.05

A30C

 0.2 0.1 0.05

 30 100 300 1000

A31C

 0.5 0.2 0.1 0.05

A32C

 0.5 0.2 0.1 0.05

 30 100 300 1000

B30C

 0.5 0.2 0.1 0.05

B31C

 0.5 0.2 0.1 0.05

 30 100 300 1000

B32C

 0.5 0.2 0.1 0.05

B33C

 0.5 0.2 0.1

C20C

 0.5 0.2 0.1 0.05

C30C

 0.5 0.2 0.1

C31C

 0.5 0.2 0.1

C32C

 0.2 0.1 0.05

D20C

 0.2 0.1 0.05

D30C

 0.2 0.1 0.05

D31C

 0.5 0.2 0.1

D32C

 0.2 0.1 0.05

D33C

 0.2 0.1 0.05

D34C

 0.1 0.05 0.02

E20C

 0.1 0.05 0.02

E21C

 0.1 0.05 0.02

E30C

 0.1 0.05 0.02

E31C

 0.5 0.2 0.1

F20C

 0.2 0.1

F21C

 0.2 0.1

F22C

 1 0.5 0.2 0.1

F30C

 1 0.5 0.2 0.1

F31C

 0.5 0.2 0.1

G30C

 0.2 0.1

G31C

 0.5 0.2

H30C

 0.2 0.1 0.05

J20C

 0.2 0.1 0.05

J30C

 0.2 0.1 0.05

J31C

 0.2 0.1 0.05

J32C

 0.2 0.1 0.05 0.02

J33C

 0.5

L30C

 0.5 0.2

L31C

 0.5 0.2

M20C

 0.5 0.2

M21C

 0.5 0.2

M30C

 0.5 0.2 0.1 0.05

P30C

 0.5 0.2

Q30C

 0.5

S20C

 0.5

T30C

lifetime.mugshot.1recent.mugshot.1

lifetime.webcam.1recent.webcam.1

lifetime.mugshot.50recent.mugshot.50

lifetime.webcam.50recent.webcam.50 Figure 9: Identification accuracy declines with the number of enrolled subjects. The figures plot miss rate vs. population size, N, i.e FNIR(N, R, 0, L) with R = 50 (dotted lines) and R = 1 (solid). Note the vertical scales vary for each algoritm. Each panel corresponds to one algorithm. The (upper) red lines correspond to searching poor quality webcam images. The (lower) blue lines correspond to searching better quality mugshot images.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 30

ENROL ALL LIFETIME PHOTOS MOST RECENT PHOTO

RANK=1 WEBCAM MUGSHOT VISA MUGSHOT

ALG N=160K N=640K N=1600K N=20K N=160K N=640K N=1600K N=20K N=20K N=160K N=640K N=1600K

A20C 0.351 0.395 170.429 0.099 0.127 0.150 220.166 160.101 0.119 0.145 0.167 220.182

A30C 0.286 0.331 160.362 0.087 0.116 0.139 210.155 110.084 0.105 0.133 0.156 200.172

A31C 0.287 0.331 150.362 0.087 0.116 0.139 200.155 120.084 0.105 0.133 0.156 190.172

A32C 0.382 0.431 190.467 0.105 0.132 0.156 230.172 240.124 0.140 0.173 0.200 250.216

B30C 0.592 0.634 240.658 0.081 0.100 0.113 170.123 230.123 0.097 0.120 0.136 170.147

B31C 0.592 0.634 230.658 0.081 0.100 0.113 160.123 220.123 0.097 0.120 0.137 160.147

B32C 0.488 0.531 210.560 0.071 0.091 0.104 150.113 180.106 0.085 0.109 0.125 140.136

B33C 0.488 0.531 200.560 0.071 0.091 0.104 140.113 170.106 0.085 0.109 0.125 150.136

C20C 0.768 0.793 280.810 0.153 0.177 0.194 270.206 330.244 0.179 0.208 0.229 270.243

C30C 0.585 0.624 220.651 0.122 0.146 0.165 250.179 260.213 0.143 0.173 0.195 240.211

C31C 0.624 0.659 260.685 0.121 0.143 0.162 240.173 250.200 0.142 0.169 0.191 230.205

C32C 0.596 0.637 250.664 0.129 0.155 0.175 260.190 280.221 0.150 0.183 0.207 260.223

D20C 0.239 0.267 9

0.287 0.060 0.068 0.075 90.080 90.066 0.073 0.081 0.089 90.096

D30C 0.245 0.273 100.290 0.059 0.067 0.073 80.080 50.061 0.068 0.078 0.087 80.093

D31C 0.251 0.276 120.295 0.057 0.065 0.071 50.077 70.064 0.068 0.077 0.084 50.091

D32C 0.376 0.415 180.445 0.106 0.128 0.144 190.155 200.115 0.125 0.150 0.168 210.180

D33C 0.248 0.274 110.291 0.058 0.066 0.072 6

0.078 60.062 0.067 0.076 0.085 60.091

D34C 0.248 0.277 130.296 0.055 0.066 0.073 70.079 80.064 0.065 0.076 0.086 70.092

E20C 0.077 0.094 4

0.108 0.025 0.032 0.038 40.042 40.026 0.029 0.037 0.044 40.049

E21C 0.077 0.094 30.108 0.025 0.032 0.038 30.042 30.026 0.029 0.037 0.044 30.049

E30C 0.079 0.097 20.108 0.024 0.029 0.032 10.035 10.017 0.028 0.034 0.037 10.041

E31C 0.079 0.095 1

0.106 0.025 0.031 0.034 20.037 20.023 0.028 0.035 0.038 20.042

F20C 0.465 37 0.232 0.282 37 360.278 0.256 0.312 36

F30C 0.356 0.678 290.867 0.165 0.214 0.603 290.843 290.232 0.194 0.247 0.618 34

F31C 0.365 0.720 300.886 0.163 0.214 0.656 300.864 320.239 0.192 0.248 0.670 43

G30C 0.423 44 0.187 0.245 44 390.308 0.220 0.285 0.333 44

G31C 0.369 38 0.171 0.219 38 270.214 0.198 0.254 0.293 37

H30C 0.451 0.504 35 0.262 0.309 0.346 35 380.285 0.300 0.353 0.392 32

J20C 0.240 0.272 140.297 0.076 0.102 0.119 180.133 190.114 0.090 0.121 0.143 180.161

J30C 0.194 0.231 7

0.253 0.061 0.079 0.093 120.104 150.097 0.070 0.091 0.109 120.122

J31C 0.199 0.237 80.265 0.062 0.082 0.099 130.110 140.092 0.071 0.094 0.113 130.127

J32C 0.174 0.207 50.229 0.053 0.069 0.083 110.093 130.088 0.062 0.081 0.097 110.108

J33C 0.173 0.204 60.229 0.052 0.067 0.081 100.091 100.082 0.060 0.079 0.095 100.107

L30C 0.691 41 0.385 0.442 41 420.348 0.428 0.490 40

L31C 0.640 40 0.339 0.398 40 410.332 0.382 0.444 39

M20C 0.586 0.625 32 0.319 0.371 0.412 32 370.279 0.346 0.412 0.458 29

M21C 0.561 0.598 39 0.304 0.355 0.393 39 340.273 0.327 0.390 0.435 38

M30C 0.547 33 0.285 0.342 33 350.273 0.327 0.390 0.435 30

P30C 0.361 0.408 270.728 0.093 0.123 0.147 280.564 210.117 0.120 0.151 0.172 33

Q30C 0.551 42 0.303 0.361 42 400.309 0.343 0.405 41

S20C 0.796 43 0.467 0.523 43 440.420 0.512 0.569 42

T30C 0.650 0.692 34 0.391 0.449 0.492 34 430.351 0.432 0.495 0.540 31

Table 10: Rank-1 miss rates: The table gives FNIR values for different enrolled population sizes, three different image types, and two

enrolment strategies. At left are seven columns indicating accuracy for searches of legacy webcam images and mugshots; these results

apply where all but the most recent image of an individual is enrolled, and the last image is searched. On the right side, only the

penultimate image is enrolled, and the last image is searched (see section 3.2). The column in yellow is highlighted because it applies

to well posed visa images of individuals including children. All other columns apply to images drawn from the LEO set. The blue

superscripts are column-wise algorithm rankings. Empty cells indicate that the run was not attempted, or was unsuccessful.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 31

ENROL ALL LIFETIME PHOTOS MOST RECENT PHOTO

RANK=50 WEBCAM MUGSHOT VISA MUGSHOT

ALG N=160K N=640K N=1600K N=20K N=160K N=640K N=1600K N=20K N=20K N=160K N=640K N=1600K

A20C 0.174 0.222 170.255 0.044 0.063 0.080 210.093 160.033 0.057 0.078 0.097 210.110

A30C 0.126 0.167 150.198 0.039 0.055 0.071 190.083 5

0.019 0.049 0.067 0.085 190.098

A31C 0.126 0.167 160.198 0.039 0.055 0.071 200.083 6

0.019 0.049 0.067 0.086 200.098

A32C 0.203 0.253 180.288 0.050 0.068 0.084 220.096 180.039 0.070 0.094 0.115 230.131

B30C 0.371 0.443 250.491 0.044 0.058 0.069 180.078 240.053 0.052 0.070 0.084 180.094

B31C 0.371 0.443 240.491 0.044 0.058 0.069 170.078 230.053 0.052 0.070 0.084 170.094

B32C 0.274 0.341 210.383 0.037 0.050 0.061 160.069 200.042 0.044 0.060 0.074 150.084

B33C 0.274 0.341 200.383 0.037 0.050 0.061 150.069 190.042 0.044 0.060 0.074 160.084

C20C 0.615 0.667 280.700 0.091 0.115 0.134 270.147 390.143 0.107 0.137 0.160 270.174

C30C 0.373 0.433 220.478 0.057 0.082 0.102 250.115 310.106 0.066 0.096 0.122 250.137

C31C 0.441 0.490 260.528 0.063 0.084 0.103 240.114 300.102 0.075 0.100 0.122 240.136

C32C 0.382 0.444 230.489 0.060 0.087 0.109 260.122 330.109 0.071 0.102 0.128 260.145

D20C 0.127 0.161 110.188 0.035 0.044 0.053 130.061 110.021 0.040 0.052 0.064 130.072

D30C 0.127 0.163 120.189 0.033 0.042 0.051 120.059 80.020 0.039 0.050 0.061 110.071

D31C 0.127 0.164 140.191 0.033 0.042 0.051 100.059 70.020 0.039 0.049 0.060 100.070

D32C 0.228 0.264 190.292 0.058 0.076 0.090 230.101 220.049 0.068 0.089 0.107 220.119

D33C 0.128 0.166 130.191 0.033 0.042 0.051 110.059 9

0.020 0.039 0.050 0.061 120.071

D34C 0.121 0.151 100.174 0.030 0.038 0.046 70.054 100.020 0.035 0.046 0.056 70.064

E20C 0.032 0.042 3

0.049 0.014 0.018 0.022 30.025 30.007 0.017 0.021 0.025 30.028

E21C 0.032 0.042 20.049 0.014 0.018 0.022 20.025 20.007 0.017 0.021 0.025 20.028

E30C 0.032 0.043 10.048 0.016 0.019 0.021 10.023 10.006 0.018 0.021 0.024 10.026

E31C 0.039 0.050 4

0.058 0.018 0.021 0.024 40.026 40.007 0.020 0.024 0.028 40.031

F20C 0.251 37 0.108 0.155 37 370.130 0.118 0.173 36

F30C 0.180 0.584 290.831 0.074 0.108 0.545 290.820 250.082 0.089 0.129 0.555 34

F31C 0.180 0.636 300.855 0.074 0.108 0.606 300.844 280.085 0.088 0.128 0.616 43

G30C 0.196 44 0.078 0.119 44 360.123 0.091 0.139 0.182 44

G31C 0.187 38 0.076 0.113 38 290.087 0.088 0.132 0.168 37

H30C 0.260 0.312 35 0.136 0.183 0.219 35 380.132 0.158 0.214 0.257 32

J20C 0.107 0.134 9

0.160 0.030 0.044 0.058 140.068 170.034 0.037 0.055 0.071 140.083

J30C 0.080 0.111 7

0.135 0.027 0.039 0.050 80.058 150.029 0.031 0.044 0.056 80.067

J31C 0.080 0.110 80.135 0.027 0.039 0.050 90.058 130.023 0.031 0.044 0.056 90.067

J32C 0.067 0.096 50.117 0.024 0.034 0.044 60.051 140.024 0.028 0.039 0.051 60.059

J33C 0.066 0.094 60.117 0.023 0.033 0.042 50.049 120.021 0.027 0.038 0.048 50.057

L30C 0.489 41 0.222 0.293 41 430.173 0.254 0.334 40

L31C 0.433 40 0.188 0.253 40 410.161 0.214 0.290 39

M20C 0.377 0.436 32 0.169 0.221 0.266 32 320.108 0.174 0.245 0.297 29

M21C 0.349 0.403 39 0.162 0.211 0.252 39 340.109 0.165 0.230 0.281 38

M30C 0.333 33 0.145 0.198 33 350.109 0.165 0.230 0.281 30

P30C 0.182 0.230 270.663 0.041 0.059 0.076 280.530 210.043 0.056 0.078 0.100 33

Q30C 0.305 42 0.144 0.206 42 400.148 0.164 0.240 41

S20C 0.639 43 0.298 0.373 43 440.237 0.333 0.418 42

T30C 0.452 0.514 34 0.225 0.288 0.338 34 420.163 0.254 0.328 0.384 31

Table 11: Rank-50 miss rates: The table gives FNIR values for different enrolled population sizes, three different image types, and

two enrolment strategies. At left are seven columns indicating accuracy for searches of legacy webcam images and mugshots; these

results apply where all but the most recent image of an individual is enrolled, and the last image is searched. On the right side, only the

penultimate image is enrolled, and the last image is searched (see section 3.2). The column in yellow is highlighted because it applies

to well posed visa images of individuals including children. All other columns apply to images drawn from the LEO set. The blue

superscripts are column-wise algorithm rankings. Empty cells indicate that the run was not attempted, or was unsuccessful.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 32

False Positive Identification Rate (FPIR)

False Negative Identification Rate (FNIR)

0.05

 0.2 0.1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3

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 3221

●

●

●

●

 3158

●

●

●

●

 3095

A31C

● ● ● ●

0.3687

● ● ● ●

0.3579

● ● ● ●

0.3508

● ● ● ●

0.3687

● ● ● ●

0.3579

● ● ● ●

0.3508

● ● ● ●

0.3687

● ● ● ●

0.3579

● ● ● ●

0.3508

● ● ● ●

0.3687

● ● ● ●

0.3579

● ● ● ●

0.3508

B32C

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3

● ● ● ●

22659

● ● ● ●

21689

● ● ● ●

21441

● ● ● ●

22659

● ● ● ●

21689

● ● ● ●

21441

● ● ● ●

22659

● ● ● ●

21689

● ● ● ●

21441

● ● ● ●

22659

● ● ● ●

21689

● ● ● ●

21441

C31C

●

●

● ●

 4529

●

●

●

●

 4062

●

●

●

●

 3727

●

●

● ●

 4529

●

●

●

●

 4062

●

●

●

●

 3727

●

●

● ●

 4529

●

●

●

●

 4062

●

●

●

●

 3727

●

●

● ●

 4529

●

●

●

●

 4062

●

●

●

●

 3727

D31C

●

●

●

●

0.5305

●

●

●

●

0.5217

●

●

●

●

0.5131

●

●

●

●

0.5305

●

●

●

●

0.5217

●

●

●

●

0.5131

●

●

●

●

0.5305

●

●

●

●

0.5217

●

●

●

●

0.5131

●

●

●

●

0.5305

●

●

●

●

0.5217

●

●

●

●

0.5131

E30C

● ● ● ●

0.4866

● ● ● ●

0.4685

● ● ● ●

0.4554

● ● ● ●

0.4866

● ● ● ●

0.4685

● ● ● ●

0.4554

● ● ● ●

0.4866

● ● ● ●

0.4685

● ● ● ●

0.4554

● ● ● ●

0.4866

● ● ● ●

0.4685

● ● ● ●

0.4554

J33C

20000 160000 640000 1600000

Figure 10: Identification error tradeoff for increasing numbers of enrolled subjects. For mugshots searched against the most recent image of an individual, the figures

plot FNIR (miss rate) vs. FPIR (false alarm rate) for four population sizes, N=20K, 160K, 640K, 1.6M. Each panel corresponds to one algorithm. The short upward lines linkpoints of fixed threshold; they show how the error rates change with N. Conventional (binomial) models of identification, which regard a 1:N search as N 1:1 comparisons,indicate that only FPIR increases with N (e.g. J33C, B32C). However, some algorithms attempt to stabilize FPIR against increases in N (e.g. D31C, E30C) - and this relieves asystem operator of the need to elevate threshold with increasing N.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 33

. Tabulation: FNIR(N, R, 0, L) is tabulated exhaustively for four population sizes and, for R = 1, 50, in Tables 10 and

11.

. Graphs: This same data is plotted as FNIR(N, R, 0, L) against N in Figure 9.

. DETs: Detection error tradeoff characteristics appear for the more accurate algorithms in Figure 10. These plot

FNIR(N, L, T, L) vs. FPIR(N, T, L) for the four population sizes, and all thresholds.

. Models: The rank-based FNIR(N, R, 0, L) data is modeled using illustrative power-law models.

Discussion: It is clear from Figure 9 that mated search miss rates increase with enrolled population size and decrease

with rank. Most importantly, miss rates usually only increase slowly with population size. While this aspect is common

to other biometric modalities, it alone is largely responsible for the operational utility of face identification algorithms.

Specifically, with a ten-fold population size increase, from 160,000 to 1,600,000, the rank one miss rates only increase by

small factors, 1.2 for E30C (0.029 to 0.035, Table 10), 1.1 for D31C (0.065 to 0.071), and 1.4 for J33C (0.067 to 0.091). This

result is modeled in the next section.

The F and P algorithms have problems at the largest population size; while this may be a property of the underlying

mathematics, it may also be a (software) implementation issue.

When algorithms are configured with high thresholds to curtail false positives, the dependence on N is mostly similar.

However, Figure 10 shows that, at FPIR = 0.002, there is reduced sensitivity of FNIR on N. For example for the D31C

algorithm, as population size increases from 160,000 to 1,600,000, FNIR increases from 0.122 to 0.134, a factor of 1.1 (lifetime

mugshots, Tables 4 and 6).

Figure 10 also shows lines connecting DETs corresponding to fixed thresholds. These reveal various dependencies of FPIR

on N; this is discussed further in section 5.3.2.

5.3.1 Models of FNIR dependence on N and R

Many plots in Figure 9 are approximately linear on log-log axes. Moreover, Figure 8 shows a similar linear reduction in

FNIR with rank, again on log-log axes. These observations motivate development here of FNIR models i.e. empirical

formulae giving miss rate as a function of population size, N, and rank, R. The goal here is not to produce a definitive

and usable model of accuracy but instead to highlight sublinear scalability effects, and to support reasoning about how

accuracy varies.

For any given dataset, the coarse empirical observation is that log FNIR is approximately linear in log N and log R. This

corresponds to a power-law model:

FNIR(N, R, 0, L) = aNbR

c

(8)

where the coefficient a is an implied recognition rate for N = R = 1, b is a scalability exponent, and c quantifies the

occurrence of mates at higher (poorer) rank. Note that this model applies to the investigational application where the

threshold is relaxed T = 08, and the requested number of candidates, L, is assumed to not affect the candidates appearing

at rank R = 1 . . . L.

This model does not exhibit the correct behavior for the case N → R and, more seriously, does not capture the behavior

seen (e.g. for algorithm D20C) showing that searching to rank 50 is less worthwhile relative to rank 1 at large population

8More advanced models of how accuracy scales with N have been proposed. These include threshold as an independent variable - the goal being to

assist in setting thresholds as the enrolled population increases. These models [4, 13, 15, 16, 23] are beyond our purpose here.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 34

sizes. This implies that the scaling exponent b should itself be dependent on R.

FNIR(N, R, 0, L) = a(N − R)

b−d log RRc

(9)

The models of equations 8 and 9 are fit to the available empirical FNIR data for each algorithm, for the “recent” enrolment

type, and for mugshot and webcam acquisition types. The exponents of the fits are tabulated in Table 12. The result show

the scalability exponents of equation 8 typically has values 0.08 < b < 0.16 indicating very sublinear growth with N. The

models also reveal that human adjudication of long candidate lists (to rank R) offers diminishing returns - the exponent

c in eq. 8 has values −0.18 < c < −0.09. We caution against using any of these values to compare algorithms since the

models are inexact and lack theoretical support.

5.3.2 Dependence of FPIR on N

Figure 10 shows lines connecting DETs corresponding to fixed thresholds. These reveal that algorithms exhibit one of two

broad dependencies of FPIR on N:

. Linear: Some algorithms exhibit an almost linear dependence of FPIR, while others have some invariance to population size. In classical biometric theory, FPIR(N, T) = N FMR(T) where FMR is the one-to-one false match probability.

This is an approximation to the Binomial model of independent failures FPIR = 1−(1−FMR)

N with small FMR. The

linear dependence of FPIR on N approximately holds for algorithms from Cognitec, Neurotechnology and Toshiba.

Implementations that implement 1:N search as N 1:1 comparisons would exhibit this behavior.

. Constant: Other algorithms, including those from 3M/Cogent, Morpho and NEC, exhibit FPIR(N, T) having a complicated dependence on N, with FPIR being approximately constant (independent of N), or in some cases even reducing at larger population sizes. Such behavior can be achieved via score-normalization [15] and by implementing

results from extreme value theory - the statistics of the largest of N random samples.

In any case, owners of systems that employ non-zero thresholds will need to understand the dependence of false positive

rates on population size - the goal being to have predictable FPIR. Either approach is viable given adequate documentation

and planning.

5.4 Effect of enrolling all historical images

Methods: The LIFETIME enrolment type is defined by enrolment of Kiimages per person as described in section 3.2. By

executing the standard set of searches, FNIR can be computed over searches where the mate was enrolled with Ki =

1, 2, . . . enrolled images.

Results: Substantially reduced miss rates are measured for all algorithms as functions of K.

Discussion: The overall FNIR values reported previously for the LIFETIME enrolments represent the maximum realizable

accuracy given this data. The relevance of the results in this section is on system design policy: operators should plan

to store, enrol, and use the full lifetime history of images from an individual. The results here do not, however, answer

the question of how old an image should be before it is retired from use. While, the default guidance here is to retain all

images regardless of capture date, an ageing study is indicated to quantify un-enrolment schedules. Such a study would

need to quantify false positive consequences of retaining too many images.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 35

SIMPLE MODEL OF EQUATION 8 EXTENDED MODEL OF EQUATION 9

ALGORITHM RECENT MUGSHOT RECENT WEBCAM RECENT MUGSHOT RECENT WEBCAM

A20C 0.034 N0.120 R

−0.145 0.092 N0.109 R−0.140 0.044 (N − R)0.099+0.014 log R R−0.330 0.137 (N − R)0.080+0.019 log R R−0.392

A30C 0.027 N0.132 R

−0.158 0.060 N0.129 R−0.165 0.034 (N − R)0.114+0.012 log R R−0.315 0.090 (N − R)0.098+0.021 log R R−0.445

A31C 0.027 N0.132 R

−0.158 0.060 N0.129 R−0.165 0.034 (N − R)0.114+0.012 log R R−0.315 0.090 (N − R)0.098+0.021 log R R−0.444

A32C 0.041 N0.118 R

−0.142 0.105 N0.107 R−0.130 0.052 (N − R)0.101+0.011 log R R−0.290 0.152 (N − R)0.080+0.017 log R R−0.362

B30C 0.031 N0.110 R

−0.126 0.237 N0.076 R−0.089 0.038 (N − R)0.095+0.010 log R R−0.251 0.359 (N − R)0.045+0.018 log R R−0.329

B31C 0.031 N0.110 R

−0.126 0.237 N0.076 R−0.089 0.038 (N − R)0.095+0.010 log R R−0.251 0.359 (N − R)0.045+0.018 log R R−0.329

B32C 0.024 N0.122 R

−0.137 0.164 N0.091 R−0.109 0.030 (N − R)0.107+0.010 log R R−0.262 0.261 (N − R)0.056+0.021 log R R−0.392

B33C 0.024 N0.122 R

−0.137 0.163 N0.091 R−0.109 0.030 (N − R)0.107+0.010 log R R−0.263 0.261 (N − R)0.056+0.021 log R R−0.393

C20C 0.072 N0.088 R

−0.105 0.504 N0.036 R−0.043 0.090 (N − R)0.070+0.011 log R R−0.237 0.625 (N − R)0.019+0.009 log R R−0.157

C30C 0.042 N0.116 R

−0.131 0.248 N0.072 R−0.090 0.061 (N − R)0.087+0.019 log R R−0.377 0.358 (N − R)0.044+0.016 log R R−0.302

C31C 0.047 N0.106 R

−0.121 0.319 N0.056 R−0.071 0.062 (N − R)0.083+0.014 log R R−0.302 0.403 (N − R)0.039+0.010 log R R−0.200

C32C 0.044 N0.116 R

−0.130 0.255 N0.071 R−0.088 0.063 (N − R)0.088+0.018 log R R−0.363 0.363 (N − R)0.045+0.015 log R R−0.293

D20C 0.025 N0.095 R

−0.096 0.061 N0.113 R−0.127 0.038 (N − R)0.063+0.019 log R R−0.340 0.107 (N − R)0.071+0.027 log R R−0.485

D30C 0.023 N0.098 R

−0.098 0.063 N0.110 R−0.130 0.033 (N − R)0.069+0.017 log R R−0.319 0.112 (N − R)0.068+0.027 log R R−0.493

D31C 0.024 N0.095 R

−0.096 0.064 N0.110 R−0.134 0.035 (N − R)0.064+0.018 log R R−0.328 0.119 (N − R)0.064+0.029 log R R−0.528

D32C 0.042 N0.104 R

−0.119 0.135 N0.087 R−0.110 0.053 (N − R)0.085+0.012 log R R−0.268 0.162 (N − R)0.074+0.008 log R R−0.220

D33C 0.022 N0.100 R

−0.092 0.067 N0.106 R−0.130 0.032 (N − R)0.071+0.017 log R R−0.308 0.123 (N − R)0.061+0.029 log R R−0.512

D34C 0.022 N0.100 R

−0.115 0.077 N0.098 R−0.152 0.030 (N − R)0.077+0.015 log R R−0.303 0.113 (N − R)0.069+0.019 log R R−0.408

E20C 0.008 N0.124 R

−0.140 0.010 N0.171 R−0.210 0.009 (N − R)0.120+0.002 log R R−0.169 0.012 (N − R)0.156+0.011 log R R−0.359

E21C 0.008 N0.124 R

−0.140 0.010 N0.171 R−0.210 0.009 (N − R)0.120+0.002 log R R−0.169 0.012 (N − R)0.156+0.011 log R R−0.359

E30C 0.012 N0.083 R

−0.112 0.014 N0.145 R−0.206 0.012 (N − R)0.085+−0.001 log R R−0.094 0.018 (N − R)0.128+0.013 log R R−0.377

E31C 0.011 N0.089 R

−0.083 0.013 N0.149 R−0.163 0.012 (N − R)0.087+0.001 log R R−0.101 0.017 (N − R)0.131+0.012 log R R−0.324

F20C 0.069 N0.128 R

−0.164

- 0.099 (N − R)

0.096+0.022 log R R−0.409

-

F21C 0.046 N0.142 R

−0.177

- 0.057 (N − R)

0.122+0.014 log R R−0.334

-

F22C 0.046 N0.142 R

−0.178

- 0.056 (N − R)

0.123+0.013 log R R−0.326

-

F30C 0.000 N0.653 R

−0.046 0.000 N0.614 R−0.058 0.001 (N − R)0.453+0.116 log R R−1.574 0.002 (N − R)0.445+0.096 log R R−1.321

F31C 0.000 N0.733 R

−0.037 0.000 N0.654 R−0.052 0.001 (N − R)0.509+0.127 log R R−1.710 0.001 (N − R)0.469+0.105 log R R−1.429

G30C 0.048 N0.148 R

−0.175 0.087 N0.133 R−0.172 0.068 (N − R)0.119+0.020 log R R−0.427 0.139 (N − R)0.097+0.025 log R R−0.495

G31C 0.047 N0.139 R

−0.160 0.092 N0.117 R−0.150 0.065 (N − R)0.112+0.018 log R R−0.385 0.134 (N − R)0.088+0.019 log R R−0.394

H30C 0.103 N0.102 R

−0.125 0.138 N0.101 R−0.127 0.140 (N − R)0.077+0.016 log R R−0.315 0.178 (N − R)0.081+0.012 log R R−0.286

J20C 0.019 N0.151 R

−0.184 0.046 N0.137 R−0.172 0.024 (N − R)0.133+0.013 log R R−0.357 0.069 (N − R)0.107+0.021 log R R−0.457

J30C 0.015 N0.147 R

−0.172 0.031 N0.153 R−0.181 0.019 (N − R)0.128+0.014 log R R−0.352 0.048 (N − R)0.120+0.024 log R R−0.499

J31C 0.015 N0.151 R

−0.180 0.032 N0.153 R−0.184 0.019 (N − R)0.133+0.013 log R R−0.345 0.051 (N − R)0.118+0.025 log R R−0.518

J32C 0.013 N0.147 R

−0.169 0.026 N0.159 R−0.193 0.017 (N − R)0.128+0.013 log R R−0.343 0.044 (N − R)0.120+0.029 log R R−0.585

J33C 0.013 N0.149 R

−0.174 0.027 N0.155 R−0.197 0.016 (N − R)0.132+0.012 log R R−0.333 0.045 (N − R)0.116+0.029 log R R−0.585

L30C 0.169 N0.091 R

−0.110

- 0.228 (N − R)

0.064+0.016 log R R−0.291

-

L31C 0.140 N0.099 R

−0.122

- 0.192 (N − R)

0.070+0.018 log R R−0.318

-

M20C 0.112 N0.108 R

−0.130 0.214 N0.083 R−0.104 0.156 (N − R)0.081+0.018 log R R−0.343 0.299 (N − R)0.057+0.016 log R R−0.305

M21C 0.105 N0.109 R

−0.131 0.198 N0.086 R−0.110 0.147 (N − R)0.081+0.017 log R R−0.343 0.280 (N − R)0.059+0.016 log R R−0.319

M30C 0.105 N0.109 R

−0.131 0.199 N0.085 R−0.110 0.147 (N − R)0.081+0.017 log R R−0.343 0.283 (N − R)0.058+0.017 log R R−0.322

P30C 0.031 N0.131 R

−0.159 0.096 N0.112 R−0.148 0.042 (N − R)0.105+0.017 log R R−0.370 0.139 (N − R)0.083+0.019 log R R−0.393

Q30C 0.106 N0.115 R

−0.151

- 0.161 (N − R)

0.078+0.025 log R R−0.430

-

S20C 0.246 N0.072 R

−0.090

- 0.321 (N − R)

0.048+0.014 log R R−0.244

-

T30C 0.176 N0.086 R

−0.103 0.302 N0.065 R−0.082 0.231 (N − R)0.064+0.013 log R R−0.266 0.389 (N − R)0.045+0.011 log R R−0.227

Table 12: Approximate models of FNIR. For each algorithm, the table gives the power-law estimates (equations 8 and 9) as estimated

using non-linear least squares. The formulae apply to LEO images searched against enrolment sets comprised of the most recent image

of N = 20, 000 . . . 1, 600, 000 individuals. The power-law functional form is chosen on the basis of the approximately straight lines

observed in Figures 9 and 8. Caution: These formulae represent empirical models that lack theoretical support. It would be an abuse to

evaluate these formulae at values N > 1.6 million. In addition, the formulae only approximate the actual measured values.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 36

5.5 Accuracy dependence on subject age

Methods: The VISA dataset includes age information. As shown in the second entry of Table 3, one image of each of the N

= 19972 individuals is enrolled. Thereafter, one mated search is conducted per person to allow FNIR estimation. Finally,

203,082 nonmated searches are run to support FPIR measurement.

We compute accuracy by age group. We define seven age groups corresponding to stages in life in which facial appearance

is similar, and between which there is consensus that appearance is usually different9. We attach informal labels to these,

as shown in Table 13. The age of the individual at the time the search image was collected is used to determine the age

Group Group Age Search Mated Time Mated

No. Label Range Age Mean Lapse Mean Count

1 baby [0, 3) 2.3 1.6 57

2 kid [3, 8) 5.7 2.8 340

3 pre [8, 13) 10.7 3.7 533

4 teen [13, 19) 17.0 2.5 1447

5 young [19, 30) 25.4 2.0 5930

6 parents [30, 55) 40.5 2.1 8293

7 older [55, 101) 63.6 2.2 2709

Table 13: Age groups: Labels and sizes of age groups to which search

images are assigned. Values in columns 3 to 5 are in years.

group bin. The enrolled image is acquired some time

before that - statistics on the time elapsed between

mated pairs appears in Table 13. FNIR is computed

over those pairs via the normal definition of equation (3). The elapsed times are too short for the study

to quantify longitudinal ageing effects. The results

for young subjects are affected by ageing because the

elapsed times, of 1-4 years are considerable in an infant.

Nonmate searches produce top scoring enrolled candidates that vary by algorithm. FPIR is computed using equation (1), applied for each search-image age group. The distribution of the age difference between the enrolment

images and whatever non-mates are returned in the search is algorithm-specific, and is not documented in this report.

Results: The results appear in two places: For six, more accurate, algorithms, Figure 11 shows detection error tradeoff

characteristics for the seven age groups. Appendix A shows identical graphs for all algorithms.

Discussion: Regarding the figures, the notable points are:

• Recognition is progressively easier with advancing age: All algorithms exhibit a strong dependence of FNIR on

age. This effect is very large, spanning a factor of ten from infant to senior, and a factor of around five from teen to

senior. Miss rates for older persons are very low: at a fixed FPIR of 0.005, the most accurate algorithm, E30C, gives

FNIR of 0.008 for persons over age 55, 0.027 for young 20-somethings, and 0.057 for teenagers. For younger persons,

the miss rates climb rapidly to 0.29 for pre-teens, 0.4 for kids, to 0.7 for babies. This progression is common to all

algorithms.

• Young children are more difficult to recognize: Identification miss rates (FNIR) ascend rapidly for pre-teens, kids

and the youngest individuals. For the baby group, 0 to about 3 years old, identification fails more often that it

succeeds, i.e. FNIR is above 50%. While the sample size is small (57 subjects), error rates are so high that the result

remains significant. This result applies for image pairs collected on average 1.6 years apart (Table 13) and will be in

considerable part due to the craniofacial shape change associated with rapid growth. The extent to which smooth

“feature-less” skin texture affects FNIR is unknown. Likewise the pose variations inherent in photographing children

have not been quantified.

• Young children are more difficult to discriminate: All of the algorithms exhibit higher false positive identification

rates for younger subjects. The grey lines in Figure 11, which link points of equal threshold, slope upwards to the

9We are prevented from using Shakespeare’s seven ages of man from the All The World’s a Stage passage in As You Like It.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 37

right, indicating simultaneously that younger subjects are less easy to recognize as themselves but also less easy to

tell apart. This indicates that younger individuals are more difficult to discriminate from other individuals.

• False positive identification rate excursions: For algorithms C31C and J33C in particular, the reduction of FPIR at a

fixed threshold continues progressively throughout adulthood. For example, using algorithm C31C configured with

threshold 21539, FPIR reduces from 0.2 in infants, to 0.05 in teenagers, to below 0.002 in seniors.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 38

False Positive Identification Rate (FPIR), A31C

False Negative Identification Rate (FNIR), A31C

0.005

0.050.020.01

 0.5 0.2 0.1

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

 3058

●

●

●

●

● ●

●

 3087

●

●

●

●

● ●

●

 3112

●

●

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●

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 3151

●

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 3174

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●

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●

●

●

●

 3197

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●

●

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 3219

babykidpreteenyoungparentsolder

False Positive Identification Rate (FPIR), B32C

False Negative Identification Rate (FNIR), B32C

0.005

0.050.020.01

 0.5 0.2 0.1

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.3435

●

●

●

●

● ●

●

0.3458

●

●

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●

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0.3481

●

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●

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0.3519

●

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0.3545

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●

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●

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●

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0.357

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●

●

●

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●

babykidpreteenyoungparentsolder

False Positive Identification Rate (FPIR), C31C

False Negative Identification Rate (FNIR), C31C

0.005

0.050.020.01

 0.5 0.2 0.1

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

21237

●

●

●

●

●

●

●

21344

●

●

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●

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●

●

21429

●

●

●

●

●

●

●

21539

●

●

●

●

●

●

●

21597

●

●

●

●

●

●

21652

●

●

●

●

●

●

babykidpreteenyoungparentsolder

False Positive Identification Rate (FPIR), D31C

False Negative Identification Rate (FNIR), D31C

0.005

0.050.020.01

 0.5 0.2 0.1

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

 3598

●

●

●

●

●

●

●

 3733

●

●

●

●

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●

●

 3863

●

●

●

●

●

●

●

 4075

●

●

●

●

●

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 4222

●

●

●

●

●

●

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 4374

●

●

●

●

●

●

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 4531

babykidpreteenyoungparentsolder

False Positive Identification Rate (FPIR), E30C

False Negative Identification Rate (FNIR), E30C

0.005

0.050.020.01

 0.5 0.2 0.1

0.001 0.003 0.01 0.03 0.1 0.3 1

●

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0.5264

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●

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●

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●

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0.5295

babykidpreteenyoungparentsolder

False Positive Identification Rate (FPIR), J33C

False Negative Identification Rate (FNIR), J33C

0.005

0.050.020.01

 0.5 0.2 0.1

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

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0.438

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0.4446

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0.4506

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0.4594

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0.4695

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●

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0.4745

babykidpreteenyoungparentsolder

Figure 11: Older subjects are more easily identified: For six of the more accurate algorithms operating on VISA images, each panel includes seven traces which partition the

population by subject age at the time of collection of the search image. The age bin labels correspond to age ranges of: [0, 3), [3, 8), [8, 13), [13, 19), [19, 30), [30, 55), [55, 101),

respectively. Both FNIR and FPIR appear on log axes. The enrolled population size is N = 19972. The grey lines link points of equal threshold. Traces toward the bottomindicate lower FNIR - subjects are more easy to recognize as themselves. Traces toward the left indicate lower FPIR - subjects are more easy to discriminate from others.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 39

5.6 Accuracy of sketch recognition

Sketches have long been used in criminal investigations. Historically the most common occurrence is for a forensic artist

to interview an eye-witness and, iteratively, produce a likeness of the individual recollected by the witness. The result, a

forensic sketch, is essentially a pencil drawing. Nowadays, commercial software is typically used to the same ends, and

this results in a composite sketch.

This section addresses whether such sketches can be matched, in one-to-many mode, against photographs resident in a

mugshot database.

Methods: We leverage photos from the very commonly used FERET database and a set of sketches of those FERET images

published by the City University of Hong Kong (CUHK). A pair is shown in Figure 3. Given photographs of n = 840

sketched individuals and N = 640, 000 mugshots of different individuals (from the LEO dataset, we enrolled n + N

photographs. We then searched a) n different frontal photographs from the FERET database; and b) the n CUHK sketches

of the enrolled FERET images. Searches produced L = 50 candidates. This experimental design is similar to that used in

prior algorithm evaluation of sketch identification algorithms [18]. That study enrolled n = 75 plus N = 10, 000 mugshots

and searched both forensic and composite sketches with L = 200.

Results: Table 14 shows miss rates for the photo and sketch searches. The values are FNIR(n + N, R, 0, L) for R =

{1, 10, 50}, and L = 50.

Discussion: Sketch identification accuracy is far inferior to that documented for mugshots elsewhere in this report, particularly Table 5, for N = 640K.

. Use of non-sequestered, public-domain, images: The FERET images and sketches are all in the public domain so, in

principle, the results could be manipulated either via training, or outright memorization. This is unlikely, however,

since the algorithm providers had no prior reason to suspect that FERET images or sketches would be part of the

FRVT evaluation.

. Use of algorithms for tasks they were not designed: As sketch-identification was never declared to be part of the

study, the algorithms are being used in a manner not expressly intended by the providers. Such “off label” usage10 is

in fact operational reality - automated algorithms are being used to recognize sketches in many police departments.

. High miss rates: The rank-1 identification miss rates are usually above 90%. The most accurate result, for A20C

from 3M/Cogent, misses the mate 89.7% of the time. At rank 50, the best result, for that same algorithm, is 73.3%

of mates are missed, i.e. only 26.7% are hit. Note the most accurate mugshot algorithms are not the most accurate

sketch identification algorithms.

. High miss rates may nevertheless be useful: The poor accuracy figures have to be compared with not having an

automated search capability at all. In such cases, the miss rate is 100% and investigative leads have to be developed

without automated face recognition. As such, the 26.7% hit rate represents a very useful resource in otherwise cold

cases. The workload associated with human adjudication of long candidate lists is considered, for mugshots, in

section 5.7

. An upper bound on accuracy: The fact that the sketches were prepared by an artist viewing the exemplar photograph probably means that the accuracy measurements here represent a “best case” upper bound on accuracy. That

said, the artist did not attempt to produce a photo-realistic image of the person but instead introduced a “shape

10The term “off label” is used in the medical community to indicate that a drug developed for one condition is prescribed by a doctor for a different,

often unrelated, condition.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 40

Enrolled INVESTIGATION FNIR

N=640000 PHOTOGRAPH SKETCH

ALG RANK=1 RANK=10 RANK=50 RANK=1 RANK=10 RANK=50

A20C 0.014 0.001 0.001 0.897 0.803 0.729

A30C 0.014 0.001 0.001 0.900 0.821 0.750

A31C 0.014 0.001 0.001 0.900 0.821 0.750

A32C 0.016 0.001 0.001 0.959 0.912 0.859

B30C 0.017 0.005 0.001 0.994 0.983 0.964

B31C 0.017 0.005 0.001 0.994 0.983 0.964

B32C 0.017 0.002 0.001 0.987 0.967 0.948

B33C 0.017 0.002 0.001 0.987 0.967 0.948

C20C 0.024 0.007 0.003 0.999 0.993 0.985

C30C 0.020 0.003 0.003 0.948 0.873 0.815

C31C 0.021 0.005 0.002 0.964 0.922 0.890

C32C 0.022 0.003 0.002 0.957 0.899 0.842

D20C 0.014 0.001 0.001 0.950 0.883 0.808

D30C 0.014 0.001 0.001 0.969 0.903 0.823

D31C 0.014 0.001 0.001 0.952 0.879 0.806

D32C 0.017 0.002 0.002 0.981 0.960 0.929

D33C 0.014 0.001 0.001 0.963 0.899 0.831

D34C 0.014 0.001 0.001 0.960 0.899 0.809

E20C 0.014 0.001 0.001 0.903 0.823 0.738

E21C 0.014 0.001 0.001 0.903 0.823 0.738

E30C 0.015 0.001 0.001 0.923 0.855 0.763

E31C 0.014 0.001 0.001 0.920 0.863 0.803

F20C 0.506 0.494 0.494 0.953 0.902 0.845

F30C 0.495 0.485 0.480 0.951 0.894 0.850

F31C 0.563 0.555 0.550 0.957 0.901 0.858

G20C 0.090 0.050 0.033 0.978 0.941 0.901

G30C 0.051 0.016 0.009 0.973 0.935 0.852

G31C 0.079 0.034 0.017 0.959 0.898 0.844

H30C 0.070 0.041 0.027 0.963 0.907 0.833

I20C 0.022 0.008 0.005 0.942 0.892 0.820

J20C 0.017 0.001 0.001 0.936 0.865 0.787

J30C 0.020 0.002 0.001 0.974 0.945 0.909

J31C 0.020 0.002 0.001 0.977 0.948 0.909

J32C 0.016 0.002 0.001 0.957 0.907 0.865

J33C 0.016 0.002 0.001 0.957 0.907 0.865

M30C 0.058 0.037 0.031 0.967 0.929 0.867

P30C 0.019 0.006 0.005 0.957 0.921 0.872

Q20C 0.466 0.087 0.049 1.000 0.993 0.973

S20C 0.169 0.105 0.070 0.994 0.984 0.971

T30C 0.088 0.056 0.051 0.979 0.937 0.888

Table 14: Sketch searches mostly fail: FNIR “miss rates” for two kinds of searches made into an enrolled dataset of N = 640,864

identities made up of 864 FERET photographs, and 640,000 background LEO mugshots. The first set is comprised of 864 searches of

different frontal FERET photographs; the second set is of 864 CUHK sketches of those FERET photographs. The accuracy results for photo

searches give very low error rates befitting good quality photographs. The sketch results are very poor - green shading is used to show

miss rates below 90%, 85% and 80% at rank 1, 10 and 50 respectively. These thresholds are arbitrary.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 41

exaggeration” to the face. While this does not go as far as a caricature artist would go, the change in appearance

may or may not be representative of the situation in contemporary law enforcement processes where software is

increasingly capable, but eye-witness recollections remain variable.

. Algorithm comparison: Nevertheless, our use of this image set probably does reveal differences in algorithmic

capability. Variation will in part depend on which facial information is represented. Prior work in this area [18] compared algorithms with and without landmark-based representations, the former outperforming the latter. Parties

interested in establishing a sketch identification facility should inquire with their providers for algorithms specifically developed for sketch identification.

. FERET photo recognition is very good: Many of the recognition algorithms give very low miss rates on the FERET

photographs. This occurs because the FERET fa-fb frontal mated pairs are of good quality, better than that for the

LEO images studied earlier in this report.

5.7 Human workload for candidate list adjudication

In a law enforcement scenario, for example, a human reviewer is usually employed to review the candidates returned

from an identification search. Typically, the reviewer inspects the search image, and compares each candidate with that

image, usually proceeding in the order of descending similarity score, and stopping when he is able to positively confirm

a mate. The length of the candidate list may be fixed for all searches, or variable, depending on system configuration. The

reviewer sometimes has the option to request a number of candidates, up to a certain limit. Variable length lists arise as a

result of applying a threshold.

The following subsections advance models for the workload and costs associated with reviewer-led identification searches.

Assumptions: This workload model assumes the following:

. The candidates are reviewed serially, not all at once in a large screen GUI, for example.

. The candidates are searched in decreasing order of similarity score, whether or not the reviewer is presented with

the score.

. Reviewers will stop after confirming a mate.

. The database is correctly consolidated such that the number of mates is zero or one.

. Reviewers always find a mate if it is present, and reviewers do not incorrectly associate a search with a nonmate

candidate. Particularly, reviewer success is independent of the natural prior occurence of a mate. Effects of fatigue

and boredom have been reported when this quantity is very low [20].

. The time taken to confirm or exclude a candidate is independent of the rank of the candidate.

. The time taken to confirm or exclude a candidate is independent of population size. This is potentially incorrect

5.7.1 Fixed length candidate lists, threshold independent workload

For now, assume also that the reviewer is not provided with, or ignores, similarity scores, and thresholds are not applied.

Suppose an automated face identification algorithm returns L candidates, and a human reviewer is retained to examine up

to R candidates, where R ≤ L might be set by policy, preference or labor availability. Given the algorithm typically places

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 42

mates at low (good) ranks, the number of candidates a reviewer can be expected to review can be derived as follows. Note

that the reviewer will:

. Always inspect the first ranked image Frac. reviewed = 1

. Then inspect those candidates where mate not confirmed at rank 1 Frac. reviewed = 1-CMC(1)

. Then inspect those candidates where mate not confirmed at rank 1 or 2 Frac. reviewed = 1-CMC(2)

etc. Thus if the reviewer will stop at after a maximum of R candidates, the expected number of candidate reviews is

M(R) = 1 + (1 − CMC(1)) + (1 − CMC(2)) + . . . + (1 − CMC(R − 1)) (10)

= R −

R

X−1

r=1

CMC(r) (11)

A recognition algorithm that front-loads the cumulative match characteristic will offer reduced workload for the reviewer.

This workload is defined only over the searches for which a mate exists. In the cases where there truly is no mate, the

reviewer would review all R candidates. Thus, if the proportion of searches for which a mate does exist is β, which in the

law enforcement context would be the recidivism rate [2], the full expression for workload becomes:

M(R) = β

 

R −

R

X−1

r=1

CMC(r)

!

+ (1 − β)R (12)

= R − β

R

X−1

r=1

CMC(r) (13)

Results: Tables 4, 5 and 6 include values for this expression in columns labelled WORK.

Importantly, we restrict the analysis to the case of equation (11) where there is always a mate, i.e. β = 1. This is done

because the goal is to compare algorithms. Note that if β < 1, reviewers will have to review more candidates than are

plotted here. Indeed when β = 0 all candidates will be reviewed regardless of which algorithm is used.

The tables show that if a reviewer is willing to review, R = 50 candidates, then the expected number of candidates actually

needing review will often be fewer than 10. For mugshot searches into an enrolled database of 1.6 million identities,

enrolled with their most recent image, the NEC algorithms necessitate reviewers adjudicate around 1.6 candidates.

Cost implications: The above expressions for reviewer workload could be multiplied by suitable time and salary factors

to estimate cost. Such a cost formulation should be extended to capture the cost of missing a mate altogether - this is a

societal cost of failing to find a mate in the first L candidates.

Conclusions: The use of more accurate face recognition algorithms implies decreased workload for human reviewers

retained to adjudicate candidate lists. The expected number of candidates before a mate is found is a useful performance

metric for identification systems.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 43

5.7.2 Workload with thresholded variable length candidate lists

In this section, the effect of thresholding candidate lists is considered. This has the potential to reduce workload further

but at the expense of increased FNIR. If a score threshold is applied to a candidate list, either by the system or by the

reviewer, the number of candidates that remain will be a random variable. In the previous section, that number was fixed

at R; here it becomes Ri(T) i.e. the number of candidates on the i-th candidate list that have score greater than or equal

to T. The workload associated with adjudication of that list is given by equation 11 as M(Ri(T)). An accuracy loss arises,

however, because low-scoring mates that are present on the full candidate list will not be available to the reviewer. This is

stated by FNIR(N, Ri(T), T, L).

Results: Figure 12 shows this workload reduction, accuracy loss, tradeoff by plotting the two quantities parametrically

with threshold, T.

Discussion: The notable observations are:

. Best result: For the D31C algorithm with an enrolled population of N = 1.6 million, workload can be reduced by

60% (to a factor of 0.4 times the baseline level) if a 5% increase in miss rates is tolerable.

. Algorithm differences: For other algorithms the cost benefit position is worse. For the A31C, B32C, and C31C

implementations the same 60% worklaod reduction gives around a 20% increase in FNIR. The position with E30C is

more complicated: The algorithm is very accurate, and baseline workload is low, so the available benefits are limited.

. Effect of population size: The tradeoff associated with thresholding is most pronounced at large population size.

This occurs because the baseline workload is higher at large N (because mates are more likely to be displaced from

rank-1 position when N is large). Thus for workload to be reduced to 0.4 times baseline, FNIR values are increased

to as high as 1.4 times the baseline when N = 20,000.

. Magnitude of the effect: The workload benefits are substantial - a reduction in workload by a factor of 0.5 corresponds to half the labor requirement, at least in applications where high volumes are sustained. The FNIR increases

are relatively low, compared to the multipliers associated with a) the use of webcam images and b) the use of inferior

recognition algorithms. They are comparable with the use of “recent” vs. “lifetime” enrolment types.

It it clear that candidate list reduction via thresholding can reduce the amount of work a reviewer does, but it will also

reduce the number of hits found, and beneficially reduce the number of false alarms.

5.8 Impostor distribution stability

Section 4.1 defines false positive identification rate (FPIR) as the fraction of searches that yield one or more false matches.

Likewise, selectivity (SEL) is defined as the expected number of false matches produced in a search. Both of these quantities are a function of threshold, with higher thresholds giving reduced values for FPIR and SEL.

As some systems are configured with thresholds that target a known (often low) FPIR [1], the operational question arises

of how stable FPIR is with changes to the properties of images or individuals that are used with the system. This issue

is determined by the stability of the nonmate distribution i.e. whether FPIR is independent of image quality, population

demographics and ethnicity.

Most of the academic literature addresses improvement of Type 1 error rates such as better hit rates. The primary performance metrics are 1:1 FNMR at fixed FMR, and closed-set CMC. The importance of a stable impostor distribution has

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 44

Fractional reduction in workload by using a threshold vs. not

Fractional increase in FNIR due to threshold

 1.1

 1.2

 1.3

 1.4

 1.5

 1.6

 0.4 0.6 0.8 1

A31C D31C

 0.4 0.6 0.8 1

C31C

B32C E30C J33C

N

20000 160000 640000 1600000

Figure 12: Reviewer workload reductions with thresholding: With recent LEO mugshots enrolled, the graphs plot on the x-axis the

reduction in the number of candidates (workload eq. (11)) that would require human review if the candidate list were reduced in length

by thresholding vs. if the full L = 50 candidates were eligile for review. On the y-axis, is the reduction in accuracy i.e. the fractional

increase in miss rates incurred by thresholding vs. considering all L = 50.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 45

received little attention in the academic literature [15]. However it is critical to systems configured with a fixed threshold

that are exposed to different kinds of images.

Methods: Webcam and mugshot images are used in 1:N searches, and scores from both mate and nonmate searches are

retained and used in computation of the DET characteristics plotting FNIR vs. FPIR (see equations (3) and 1).

Section 5.5 details the effect on both FNIR(T) and FPIR(T) when individuals in different age groups are identified.

Results: Figure 6 shows DETs for six more accurate algorithms. Appendix A includes DETs for each algorithm for

mugshots and webcam images. Similarly, Figure 11 gives DETs for identification by age group. In both cases, the plots

include lines connecting points of fixed threshold.

Discussion: The horizontal displacement of the grey lines is an indicator that the impostor distribution is not stable

under condition-change. The 3M/Cogent (A31C) and NEC (E30C) algorithms exhibit less than a factor of two change

in FPIR when processing webcams vs. mugshots (Fig. 6), and when processing young persons vs. old (Fig. 11). The

Neurotechnology (C31C) algorithm, on the other hand, gives more than a 100-fold increase in FPIR when processing

infants vs. seniors. The Cognitec algorithm gives a roughly 20-fold increase in FPIR when processing webcam images.

These results will manifest themselves operationally as longer candidate lists

5.9 Computational expense

5.9.1 Search duration times

Background: In most deployments, the enrolled population increases over time. This may be a continuous process or the

result of merging separate datasets. If the database doubles in size, so does the search time. This has major implications

for capital expenditure on computing hardware and ancillary equipment.

In our 2010 evaluation of face recognition algorithms [12], we showed that search speed scales sublinearly with enrolled

population size. That aside, there is little documentation of search speed in operational biometric systems. There is a large

and mature literature on fast search algorithms, although much of this is outside of the biometric arena. The term fast

refers to algorithms for which average search time increases better-than-linearly with population size N, for example as

log N.

Methods: See section 4.5. For identification trials, the algorithm was permitted to use the available hardware as it saw fit.

It could elect to start any number of threads [1,16] and this could be varied dynamically and as a function of N. Only one

provider, F, elected to use threading.

For timing measurement, both mate and nonmate searches were conducted in random order. However, the duration

measurements given here are computed over only nonmate searches. Mate searches are not used because some algorithms

implement so-called 1:FIRST semantics where the search is terminated once any sufficiently high scoring hit has been

found. This may be operationally valuable, but it confuses measurement of underlying algorithmic efficiency.

The candidate list length was fixed at L = 50. Production of longer candidate lists may take longer - this is untested in

FRVT. The estimates reported below are median values estimated over 2000 searches for which a mate exists, and 2000

searches for which a mate does not exist.

Results: Timing results are tabulated in Table 15. Graphs of search durations vs. N appear in the algorithm summaries of

Appendix A.

There is wide variation in search speed. For N = 1,600,000, the most accurate algorithm (E30C) takes 1.093 seconds to

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 46

ENROLLMENT FINALIZATION SEARCH

ALG ETIME ESIZE FEXPN FTIME FPOW SSIZE STIME Ts(20)k Ts(160)k Ts(640)k Ts(1600)k Ts = aNb

A20C 520 4106 1.0 1.8 1.7 4106 519 155 ± 1 1235 ± 0 4953 ± 1 12568 ± 97 0.0080N1.00

A30C 327 4622 1.0 8.0 1.0 4622 324 29 ± 0 242 ± 0 979 ± 1 2454 ± 0 0.0018N0.99

A31C 324 4622 1.0 12.2 1.6 4622 324 28 ± 0 243 ± 0 982 ± 2 2452 ± 0 0.0017N0.99

A32C 296 1046 1.0 0.6 1.6 1046 189 11 ± 0 102 ± 0 447 ± 3 1080 ± 0 0.0017N0.94

B30C 282 4796 1.0 3.6 1.0 4796 284 96 ± 0 756 ± 0 2972 ± 0 7494 ± 18 0.0059N0.98

B31C 287 4796 1.0 3.8 0.4 4796 282 95 ± 0 756 ± 0 2982 ± 0 7498 ± 2 0.0053N0.99

B32C 382 9932 1.0 25.3 1.1 9932 388 255 ± 0 2018 ± 0 8076 ± 1 20239 ± 33 0.0117N1.01

B33C 385 9932 1.0 235.5 0.9 9932 382 255 ± 0 2019 ± 0 8064 ± 1 20285 ± 4 0.0119N1.00

C20C 263 35994 0.0 0.0 0.9 35994 222 164 ± 3 1143 ± 15 4518 ± 65 11149 ± 170 0.0079N0.99

C30C 363 37008 0.0 0.0 1.0 37008 362 991 ± 2 7157 ± 20 28652 ± 79 72246 ± 195 0.0382N1.01

C31C 361 37008 0.0 0.0 1.0 37008 361 179 ± 2 1279 ± 17 4986 ± 65 12575 ± 154 0.0056N1.02

C32C 236 5040 0.0 0.0 0.9 37008 360 135 ± 0 1014 ± 1 4032 ± 6 10136 ± 14 0.0067N0.99

D20C 714 8005 1.0 45.5 1.0 20489 716 443 ± 0 510 ± 0 - - 228N0.07

D30C 754 8562 1.4 76.8 1.0 21046 709 722 ± 0 804 ± 0 958 ± 0 1263 ± 1 193N0.13

D31C 729 12247 1.2 88.1 1.0 24731 712 1218 ± 0 1278 ± 0 1422 ± 0 1761 ± 0 499N0.08

D32C 493 857 5.2 5.0 1.0 857 471 35 ± 0 87 ± 1 260 ± 0 605 ± 14 0.0026N0.86

D33C 733 8005 1.5 57.3 1.0 20489 712 736 ± 0 812 ± 0 954 ± 0 1271 ± 0 174N0.13

D34C 704 8005 1.3 46.7 1.0 20489 707 731 ± 0 813 ± 0 957 ± 0 1236 ± 0 182N0.13

E20C 216 2465 1.0 2.2 1.3 2465 204 15 ± 0 123 ± 0 513 ± 1 1143 ± 0 0.0058N0.85

E21C 196 2465 1.0 2.4 1.2 2465 204 17 ± 0 137 ± 0 481 ± 0 1245 ± 2 0.0004N1.04

E30C 229 2529 1.0 2.5 1.3 2529 208 22 ± 0 118 ± 0 450 ± 0 1093 ± 0 0.0021N0.92

E31C 205 2529 1.0 2.3 1.3 2529 210 13 ± 0 53 ± 0 168 ± 0 279 ± 1 0.0199N0.67

F20C 170 6760 1.0 3.2 - 6760 118 - 359 ± 9 - - 0.0120N0.87

F30C 272 6440 1.0 3.0 1.4 6440 259 196 ± 0 1529 ± 0 3217 ± 5 - 1N0.60

F31C 262 7484 1.0 76.9 1.6 7484 263 242 ± 0 1901 ± 1 3510 ± 1 - 2N0.54

G30C 186 3484 1.0 1.3 1.7 3484 110 118 ± 2 843 ± 0 3376 ± 9 - 0.0043N1.02

G31C 145 2240 1.0 0.8 1.9 2240 192 14 ± 0 111 ± 0 418 ± 12 - 0.0003N1.06

H30C 81 4420 1.0 5.9 1.9 4420 65 55 ± 0 231 ± 2 901 ± 8 - 0.0051N0.91

J20C 466 6206 1.0 444.5 1.7 6206 485 14 ± 2 117 ± 0 568 ± 52 1137 ± 1 0.0071N0.84

J30C 532 4158 1.1 1167.7 0.9 4158 528 13 ± 0 134 ± 0 552 ± 0 1110 ± 1 0.0018N0.94

J31C 526 4158 1.1 4191.1 0.8 4158 527 13 ± 0 130 ± 0 454 ± 47 1194 ± 2 0.0012N0.97

J32C 490 8254 1.0 70.5 1.0 8254 546 23 ± 0 244 ± 1 829 ± 2 2449 ± 0 0.0005N1.07

J33C 550 8254 1.1 8403.1 0.6 8254 546 30 ± 0 191 ± 0 798 ± 2 2086 ± 38 -

L30C 543 7328 1.0 151.7 1.0 7328 537 507 ± 0 4024 ± 1 - - 0.0242N1.00

L31C 626 7328 1.0 9.2 0.8 7328 629 497 ± 0 3998 ± 1 - - 0.0252N1.00

M20C 164 5608 1.0 0.1 0.0 5608 - - - - - -

M21C 164 5608 1.0 0.1 0.0 5608 - - - - - -

M30C 136 5608 1.0 0.1 0.9 5608 112 25 ± 0 179 ± 1 688 ± 0 - 0.0007N1.03

P30C 565 3713 1.0 11.0 1.0 3713 564 364 ± 0 2938 ± 1 11650 ± 8 - 0.0195N0.99

Q30C 170 5600 1.0 4.5 - 5600 170 78 ± 1 1417 ± 5 - - -

S20C 411 800 1.0 0.3 - 800 172 55 ± 0 443 ± 0 - - 0.0027N1.00

T30C 283 1936 0.0 0.0 1.0 1936 189 12 ± 0 94 ± 0 348 ± 0 - 0.0022N0.89

Table 15: Resource consumption: From left to right: the duration of enrolment template generation; enrolment template size in bytes;

the ratio of the size of post-finalized enrolment data on disk to N times ESIZE; the duration of the finalization call in seconds for N = 1.6

million; the dependence of finalization time on N i.e. b in N

b

; search template size in bytes; and its generation time; the time taken to

execute searches of the given population, N; the power-law model of search duration; All times are medians, unless stated otherwise,

and apply to the execution of API functions on a single core of a c. 2011 server-class PC-architecture processor. One exception to this is

that the 1:N search for F algorithms used up to 16 cores - those times have not been adjusted and are not therefore comparable.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 47

execute a search. The slowest algorithm by far, C30C, takes 72.2 seconds.

As shown in Appendix A, most search durations increase linearly on a log-log plot.

log T = a log N + b, (14)

This observation corresponds to a power-law form

T = cNa(15)

where the constant b = log c determines the intercept on the observed plot, and the constant a is the slope. The parameters were estimated using nonlinear least squares regression without log transformation. Uncertainty estimates are from

bootstrapping.

Discussion: By referencing the a exponents in Table 15, it is evident that with three notable exceptions, all providers’

algorithms exhibit a linear dependence of search duration on the enrolled population size. The exceptions are Morpho,

where the exponent a is typically around 0.1. This implies that a 10 fold increase in N gives a 1.25 fold increase in

search duration. While this is an attractive proposition for very large deployments (several United States’ driving license

databases have sizes above 107), the practical relevance of this will depend on the transaction volume demands and the

number of available computers. The second exception is NEC, where the exponent a varies from 0.92(E30C) down to

0.67(E31C) for which a ten fold increase in N leads to a 4.6 fold increase in duration. Finally the Tsinghua (F) algorithms

exhibit sublinear speed.

Note that if K processing cores are available on a computer, K searches cannot typically be conducted simultaneously

without some loss of speed due to memory bus bandwidth constraints.

Conclusions: Search durations scale approximately as a power of the database size. The exponents are dependent on the

algorithm. There is approximately an order of magnitude difference in the search durations measured for the four most

accurate algorithm providers. The most accurate algorithms are among the fastest.

5.9.2 Template creation times

Background: How long does it take to extract features from an image and make a template? Does this depend on the

width and height of the input image? Does it depend on whether the template is used for enrolment, verification, or

identification?

Drivers: Template generation time is often a large component of a 1:N identification search, obviously depending on N. If

multiple images are to be searched, e.g. frames from a video sequence, then template generation time can be important,

and can dominate an overall transaction time. Additionally, template generation time will be important if an existing

image corpus is going to be re-enrolled by a new provider. For example, re-enrolment of an 18M person driving license

database takes 1 x 18 x 106 / 64 / 3600 = 156 hours if a one second template generation were sustained on a 32 core blade

installation. This does not include de-duplication searches.

Methods: See section 4.5.

Results: Timing results are tabulated in Table 15. Template generation durations are graphed alongside search duration

in the algorithm summaries of Appendix A.

Discussion: Template generation times start below 100 milliseconds (H30C, Q30C) and range from 200-750 milliseconds

for the most accurate algorithms. There is some industry-wide tradeoff of template generation speed with accuracy:

Several of the more inaccurate algorithms are efficient in their production of templates (algorithms from Q, G, M, durations

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 48

below 200 msec) whereas the providers of the second and third most accurate algorithms prepare templates more slowly, J

in approximately 500 msec and D in around 700 milliseconds. This implication is contradicted however by the production

of provider E of templates in less than 250 milliseconds.

Conclusions: Template creation times are independent of the target population size, suggesting that developers did not

tailor their algorithmic representation to the size of the identification search.

5.10 Template size

Each implementation encodes information derived from the face image in a proprietary representation of the feature

data. This information is generally a trade secret. It encodes one or more mathematical representations of the face shape,

structure, or texture but could also, in principle, encode anything else (e.g. non-tradtional information such as hair color,

style, eye color).

Demand driver: Templates contain the mathematical representation of one or more images of a person. Biometric templates are proprietary, non-standard, and their content is protected as a trade-secret.

The size of the feature data is an important system-design parameter in most biometric applications. Template size is

clearly influential on storage requirements, both on-disk and in-memory, on network transmission bandwidth requirements, and on machine throughput. In addition, a large template may be associated with computational complexity and

computational expense of the matching algorithm.

Methods: The FRVT Evaluation Plan and API [14] explicitly supported measurement and reporting of facial recognition

template size. Two direct measurements of template size are made: one for enrolment templates, and one for search

templates. The API supports passage of K ≥ 1 images to the template generation function under test. KB bytes is preallocated, where maximum template size, B, was returned by an initialization function. For any given input, the exact

template size was returned and used to save the template to disk.

Results: Table 15 shows template sizes, in bytes. The values also appear alongside the accuracy results in Tables 4 - 6.

Discussion:

. Between-provider size variation: Across all providers, template sizes vary from 0.8 to 37 kilobytes, with the more

accurate algorithms having sizes between 2 and 10KB. Some providers submitted algorithms with notably small

“lightweight” templates.

. Within-provider size variation: Some providers submitted algorithms with varying template sizes. Larger templates are assumed to contain richer, more disriminative, features that should afford better accuracy.

3M/Cogent’s most and least accurate algorithms used templates of size 4622 and 1046 bytes respectively. This more

than four-fold size difference gave rank one miss rates of 0.133 and 0.173 respectively (Table 4, RECENT enrolment).

This loss is less pronounced at low FPIR and for LIFETIME enrolment.

Cognitec submitted algorithms with sizes of 9932 versus 4796 bytes. This approximately two-fold variation gave rise

to rank one miss rates of 0.109 vs. 0.120 respectively (Table 4, RECENT enrolment).

Morpho pushed this tradeoff still further by submitting one algorithm, D32C, with a template size of 857 bytes. The

best algorithm, D31C, used enrolment templates of size 12247 bytes and search templates of about twice that size,

24731 bytes. This algorithm offers very small accuracy improvements over their second most accurate algorithm

used enrolment templates of size 8005 bytes and search templates of size 20489 bytes. Thus comparing the smallest

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 49

template (D32C) with a second tier algorithm (D34C), the error rate approximately halves from 0.150 to 0.076 (Table

4, RECENT enrolment).

Toshiba explored this variation also submitting templates of size 4158 and 8254 bytes. These afforded error rates of

0.079 (J33C) and 0.091 (J30C).

In conclusion, the ability to reduce template size may be useful operationally, especially for example in speed and

bandwidth sensitive applications. Error rate increases are often modest, but increase substantially with very small

templates. Within-provider accuracy variations are usually smaller than between-provider - this is evident in the

algorithm rankings of Tables 4 - 6.

. No dependence on N: In all cases, the size of a single enrolment template is independent of the size of the enrolled

population. This shows that developers did not exploit the provision, via the API initialization call, of the integer

number of subjects about to be enrolled. This information would have allowed the implementation to use larger,

richer templates for larger N.

. Linear in K: Template sizes vary with the number of images input to the template generation function - see section

3.2. The API sends K ≥ 1 images to the template generation function. For all algorithms, the size of the enrolment

template grows linearly with the number of images that went into its creation. This indicates that algorithms are not

integrating facial information across images.

. Some asymmetric templates: The API supported asymmetric or role-specific templates. This allows a template to

be used only for enrolment, or only for search, but not vice-versa. Many template sizes are independent of role.

The exceptions are algorithm C32C and all the D algorithms except D32C. Operationally, a verification template is

usually not stored permanently as it exists only for the duration of a recognition transaction.

. Within-memory representation: Further when N images are enrolled, each producing a template of size, x, the

notional enrolment database size will be Nx. This collection of templates is sent to a one-time finalization function

provided by the algorithm. This prepares the data for subsequent searches. Specifically, it takes the Nx bytes of input

and writes F bytes to disk. This size is recorded and used to compute the expansion factor F/Nx. Some algorithms

simply copy the input data, whence the expansion factor is 1. Other algorithms re-arrange the input data for efficient

in-memory search.

5.10.1 Finalization times

Background: Finalization is a processing step that is applied over a set of N templates from N individuals. It is executed

once, before any searches are executed. It is included in the FRVT execution pipeline to allow algorithms to derive valuable

information from the entirety of the data, i.e. information that cannot be derived during the image-to-template feature

extraction operation.

Results: Finalization times are tabulated in Table 15.

Discussion: Most implementations execute finalization very quickly indicating only trivial data copying or re-arrangement.

However, some algorithms execute more slowly, particularly those of Toshiba, indicating that the finalization step is a

non-trivial mathematical operation. It is not known whether such processing could be executed in an operational context,

where it is necessary to add and delete entries from the enrolment database on an ongoing basis. The assumption is that

finalization is an operationally realistic process that could either be executed periodically during the lifetime of a biometric

system, or on-demand after a batch enrolment.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 50

Median Template Size (bytes)

FNIR(N=160000, R=1, T=0, L=50)

0.05

 0.1

 0.2

 0.5

 1000 3000 10000 30000

A20C

A31C A30C

A32C

B30C

B31C B32C

B33C

C20C

C30C

C31C

C32C

D20C

D30C D31C

D32C

D33C

D34C

E20C E21C

E30C E31C

F20C

F21C

F30C F22C

F31C

G30C

G31C

H30C

J20C

J30C

J31C

J32C

J33C

L30C

L31C

M30C

P30C

Q30C

T30C

● ●

●

●

●

●

●●

●

●● ●

●

●

●

●●

●

● ●

●

●

●

●

●●

●●

●

●

●

●

●

●

Median Template Generation Duration (msec)

FNIR(N=160000, R=1, T=0, L=50)

0.05

 0.1

 0.2

 0.5

 100 300 1000

A20C

A30C A31C

A32C

B30C

B31C

B32C

B33C

C20C

C30C

C31C

C32C

D20C

D31C D30C

D32C

D33C

D34C

E20C

E21C

E30C E31C

F20C

F21C

F30C F22C

F31C G30C

G31C

H30C

J20C

J30C

J31C

J32C J33C

L30C

L31C

M30C

P30C

Q30C

T30C

● ●●

●

●●

● ●

●

●● ●

●● ●

●

●●

● ●

●●

●

●● ● ●

●

●

●

●

●●

●●

●

●

●

●

●

●

Median 1:160000 Search Duration (msec)

FNIR(N=160000, R=1, T=0, L=50)

0.05

 0.1

 0.2

 0.5

 100 300 1000 3000

A20C

A30C A31C

A32C

B30C

B31C

B32C

B33C

C20C

C31C C30C

C32C

D20C D30C

D31C

D32C

D33C

D34C

E20C

E21C

E31C E30C

F20C

F21C

F22C F30C

F31C G30C

G31C

H30C

J20C

J30C

J31C

J32C

J33C

L30C

L31C

M30C

P30C

Q30C

T30C

● ●●

●

●

●

●

● ● ●

● ● ●

●

●●

● ● ● ●

●

● ● ● ●

●

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● ●

●

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●

Figure 13: Performance tradespaces: The plots show rank one miss rates against three measures of resource consumption: feature size,

feature extraction time, and nonmated search duration.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 51

5.11 Exploiting multiple cores

The FRVT Evaluation Plan and API document [14] did not support execution of a search across B > 1 blades because there

was no need. The reasons are as follows:

. In all cases, for all population sizes, the entire enrolment database is small enough to fit in main memory.

. A blade equipped with C > 1 cores was fully utlized by running C searches simultaneously as separate processes.

This facility was not used for threaded implementations. (Timing measurements were made with C = 1 process).

. When searching an enrolment database of size E on a blade with memory M, the number of copies of the enrolment data that can be made and kept in memory is c = bM/Ec. This supports execution of min(c, C) completely

independent processes, each running separate searches.

. However, we can avoid this memory limit by making only c = 1 copies of the enrolment database by using the LINUX

fork() system call C times. While this spawns C entirely separate processes, the LINUX implementation of fork() uses

copy-on-write semantics, which means that the enrolment data is not copied because, as a read-only element, it does

not change.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 52

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A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 53

[20] Expert Working Group on Human Factors in Latent Print Analysis. Latent print examination and human factors: Improving

the practice through a systems approach. Technical report, U.S. Department of Commerce, National Institute of Standards and

Technology, 2 2012.

[21] G. W. Quinn and P. Grother. Performance of face recognition algorithms on compressed images. NIST Interagency Report 7830,

National Institute of Standards and Technology, 12 2011. http://face.nist.gov/mbe as MBE2010 FRVT2010.

[22] Deepayan Sarkar. Lattice: Multivariate Data Visualization with R. Springer, New York, 2008. ISBN 978-0-387-75968-5.

[23] Jamie Sherrah. False alarm rate: a critical performance measure for face recognition. In FGR, pages 189–194. IEEE Computer

Society, 2004.

[24] United States. Passport photo guidelines. Department of State, 2011. http://travel.state.gov/content/passports/english/passports/photos/ph

examples.html.

[25] X. Wang and X. Tang. Face photo-sketch synthesis and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence

(PAMI), 31(11):1955–1967, 2009.

[26] Working Group 3. Ed. M. Werner. ISO/IEC 19794-5 Information Technology - Biometric Data Interchange Formats - Part 5: Face image

data. JTC1 :: SC37, 2 edition, 2011. http://webstore.ansi.org.

[27] Bradford Wing and R. Michael McCabe. Nist special publication 500-271: American national standard for information systems

data format for the interchange of fingerprint, facial, and other biometric information part 1. Technical report, September 2011.

ANSI/NIST ITL 1-2011.

[28] W. Zhang, X. Wang, and X. Tang. Coupled information-theoretic encoding for face photo-sketch recognition. In Proceedings of IEEE

Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 54

A Biometric Error Rate Tradeoff Characteristics

This Appendix is intended to give a biometric identification-specific overview of the Detection Error Tradeoff characteristic

DET. More general and detailed information is given in the Egan’s class book [8].

A detection error tradeoff (DET) characteristic represents the tradeoff between Type II and Type I classification errors. A receiver 

operating characteristic (ROC) is usually equivalent and the terms are synonymous. In biometrics, Type II errors occur when two 

samples of one person do not match – this is called a false negative. Correspondingly, Type I errors occur when samples from two 

persons do match – this is called a false positive. Matches are declared by a biometric system when the native comparison score 

from the recognition algorithm meets some threshold. Comparison scores can be either similarity scores, in which case higher 

values indicate that the samples are more likely to come from the same person, or dissimilarity scores, in which case higher values 

indicate different people. Similarity scores are traditionally computed by fingerprint and face recognition algorithms, while 

dissimilarities are used in iris recognition. In some cases, the dissimilarity score is a distance; this applies only when metric

properties are obeyed. In any case, scores can be either mate scores, coming from a comparison of one person’s samples, or 

nonmate scores, coming from comparison of different persons’ samples. The words genuine or authentic are synonyms for mate, 

and the word impostor is used a synonym for nonmate. The words mate and nonmate are traditionally used in identification 

applications (such as law enforcement search, or background checks) while genuine and impostor are used in verification 

applications (such as access control).

For iris recognition, mate comparisons yielding dissimilarities greater than a threshold are false negatives. In identification these are 

called misses and contribute to the false negative identification rate (FNIR). Nonmate comparisons at or below a threshold are false 

positives; in identification these are sometime called false alarms, and they contribute to false positive identification rate (FPIR). 

The threshold can take on any real value, and it is conventional in biometrics testing to examine error rates as a function of the 

threshold. In many systems, the threshold can be varied continuously, while in other (production) systems, it may only take on a few 

settings.

Returning to the DET, it plots a function of FNIR against a function of FPIR. Here and in many other reports, the function is the 

logarithm function (log axes). However, a DET might also plot the hit rate, and the true positive identification rate, TPIR = 1 – FNIR is 

plotted on a linear scale; this is often referred to as a ROC. More rarely, the function might be the inverse Gaussian function.

More detail and generality is provided in formal biometrics testing standards, see the various parts of ISO/IEC 19795 Biometrics 

Testing and Reporting. More terms, including and beyond those to do with accuracy, see ISO/IEC 2382-37 Information technology --

Vocabulary -- Part 37: Harmonized biometric vocabulary

Accuracy Terms + Definitions

FNIR = False Negative Identification Rate

FNIR = FNIR(N, T, L, R)

FNIR is computed by executing mate searches into an enrolled population of size N. It is 

the proportion of mate searches for which the mate is

• EITHER not returned as any of L candidates,

• OR is present but has dissimilarity above threshold T

• OR is present at rank greater than R.

In IREX III, the rank criterion is not used for DET computations, i.e. R  , so FNIR is 

solely a function of population size, N and threshold, T. FNIR(N, T).

FPIR = False Positive Identification Rate

FPIR = FPIR(N, T, L)

FPIR is computed by executing nonmate searches into an enrolled population of size N. 

It is the proportion of returned candidates which have dissimilarity at or below 

threshold T. If S searches are conducted, S x L candidates will be returned, and FPIR is 

the number at or below threshold, divided by (S x L).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 55

Type II Error Rate (1:N FNIR, 1:1 FRR or FNMR See ISO/IEC 19795-1)

Type I Error Rate (1:N FPIR, 1:1 FAR or FMR. See ISO/IEC 19795-1

Log-scale is typical to show small numbers.FNIR is a synonym for “miss rate”; the complement, 1-FNIR is the “hit rate” 

The perfect biometric: Zero errors. In biometrics, this practically never occurs.

Excellent biometric, but only after fraction, y, of mate transactions fail. y

Flat DET is desirable – false positive rate can be set arbitrarily low without increase in false negatives

Crossing DET’s indicate different shape of the tails of the impostor distribution.

Low FPIR values achieved with more stringent, thresholds.

Log-scale is almost always required because low FPIR values are operationally relevant.

FNIR

Equal Error Rate (EER) line crosses DET when FMR = FNMR. Popular as a summary accuracy statistic in 1:1 verification, it usually corresponds to an operationally unrealistic FMR. It is not a useful number for 1:N recognition.

FPIR

Flat DETs: A small change in FNIR has direct correspondence to a large change in FPIR.

DET Properties and Interpretation 1 :: Error Rates, Metrics, Comparison of algorithms

Two typical biometric systems: B is more accurate than A. This applies at all operating points along the DET.

A

B

Type I Errors1;1 matching FMR = False Match Rate1:1 transactional FAR = False Accept Rate1:N matching FPIR = False Positive Identification RateType II Errors1:1 matching FNMR = False Non-match Rate1:1 transactional FRR = False Rejection Rate1:N matching FNIR = False Negative Identification Rate Figure 14

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 56

Type II Error Rate (1:N FNIR, 1:1 FRR or FNMR See ISO/IEC 19795-1)

Type I Error Rate (1:N FPIR, 1:1 FAR or FMR. See ISO/IEC 19795-1

Log-scale is typical to show small numbers.FNIR is a synonym for “miss rate”; the complement, 1-FNIR is the “hit rate” 

Excellent biometric, but only after fraction, y, of mate transactions rejected. y

Low FPIR values achieved with lower, i.e. more stringent thresholds.

Log-scale is often required because low FPIR values are operationally relevant.

Sharp rise in DET indicates possible ground truth errors: Two or more persons share the same ID. Errors typically resolved via human inspection.

x

DET Properties and Interpretation 2 :: Non-ideal tests, datasets or systems 

A stepped DET occurs at the ends of the score ranges when FNIR and FPIR estimates are made from very few samples. At these thresholds, the uncertainty in the measurements will be larger.

For systems that produce a limited number of comparison scores, e.g. one configured with three “high”, “medium” and “low” security settings, the DET has three points.

(0,1)

A DET characteristic that just stops indicates exhaustion of the sample data, with neither FPIR nor FNIR being zero. This indicates that both genuine and impostor samples are observed at the end of the ranges.

For systems that produce only a decision, the DET has one point.

(1,0)

All DETs pass through points (0,1) and (1,0) corresponding to thresholds 0 and .

Figure 15

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 57

Type II Error Rate (1:N FNIR, 1:1 FRR or FNMR See ISO/IEC 19795-1)

Type I Error Rate (1:N FPIR, 1:1 FAR or FMR. See ISO/IEC 19795-1

Log-scale is typical to show small numbers.FNIR is a synonym for “miss rate”; the complement, 1-FNIR is the “hit rate” 

Algorithm B

DET Properties and Interpretation 1 :: Algorithms used in combination

If both Algorithms A and B are available and one is selected randomly for any given transaction then the effective DET marked in red is the overall DET. This is the lower convex hull of DET(A) and DET(B). 

Algorithm A 

Use of both Algorithms A and B for all transactions will produce a DET that is not inferior to A or B used alone.

Fused A + B. Multi-algorithm fusion is typically implemented at the score level (e.g. using ISO/IEC 29159-1 normalization information) but may also be done using ranks or decisions. Rarely template level fusion might be possible

Figure 16

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 58

A Algorithm report cards

This section details individual algorithm performance by including eight graphs on a single page. Figure 17 is a key for

those graphs, giving captions applicable to each report.

Error tradeoff by camera, enrolment type: The figure shows the

tradeoff between false negative and false positives by plotting

FNIR(T) vs. FPIR(T). This is the detection error tradeoff characteristic (DET). The plots are plotted parametrically on threshold,

T, which is swept across the entire range of genuine scores produced by the algorithm. Four traces are shown, one each of the

two enrolment types, and one each for the two capture processes.

The mostly vertical grey lines connect points of fixed threshold:

vertical lines show that only FNIR changes across those four conditions; horizontal lines show that FPIR is affected. The grey text

identifies the threshold values.

Effect of rank: The figure shows the effect of considering long

candidate lists by plotting miss rate against rank, i.e. FNIR(R).

This is closely related to a cumulative match characteristic, which

conventionally plots 1 - FNIR. Four traces are shown, one each

of the two enrolment types, and one each for the two capture

processes.

Error tradeoff by population size: The figure shows the tradeoff

between false negative and false positives by plotting FNIR(T) vs.

FPIR(T). This is the detection error tradeoff characteristic (DET).

The plots are plotted parametrically on threshold, T, which is

swept across the entire range of genuine scores produced by the

algorithm. Four traces are shown, one each population size. The

mostly vertical grey lines connect points of fixed threshold: horizontal lines show that FPIR grows linearly with population size,

as expected from classical binomial models of identification. The

grey text identifies the threshold values.

Workload savings: The figure plots two quantities parametrically with threshold. On the x-axis is the factor by which workload is reduced when a threshold is applied to shorten candidate

lists vs. when when all L = 50 candidates are retained for possible inspection. On the y-axis is the factor by which thresholding

candidate lists increases miss rate over the case of a full candidate

list, i.e. FNIR(N, R, T, L) / FNIR(N, L, 0, L).

Miss rates increase with enrolled population sizes: The figure

shows the growth of miss rates with N for two enrolment types,

two cameras, and two rank values, 1 and 50. The threshold is

set to zero, and the values are simply the proportion of mated

searches that do not yield the mate in the top R ranks. The traces

are typically straight lines on a log-log plot consistent with a

power-law behavior (see 5.3.1.

Processing time: The figure shows three traces: a horizontal line

indicating the time taken to produce a template from an image

prior to search; an ascending line indicating the time taken to

compare a template with data from N enrolled subjects; and the

sum of these two durations - the total search time. The curves

cross where N is large enough such that the 1:N search time exceeds the template generation time. All durations apply to processing on a single core of a c. 2011 server-class processor. All

templates are resident in memory.

Value of enrolling historical images: The figure shows the reduction in miss rates with the number of available enrolment images, for four population sizes. As presented in section 3.2 when

images from all historical encounters are retained and enrolled,

accuracy can be improved vs. the case where only one image is

retained. The error bars indicate confidence intervals from bootstrapping applied over searches (see section 4.6). The relevance

of this result is discussed further in section 5.4.

Selectivity vs. FPIR: As presented in section 4.1, false alarm rates

can be quantified by FPIR - the fraction of nonmate searches that

produce any candidates at or above threshold - or by selectivity -

the expected number of candidates at or above threshold. This figure shows SEL(T) vs. FPIR(T) plotted paramterically on threshold T. Selectivity is always greater than or equal to FPIR. The two

are not equal when false positives are concentrated in candidate

lists rather than being distributed across searches.

Figure 17: Key to report card figures: The boxed text of this figure describe the graphs that appear in the report cards of this Appendix.

Each report card contains 8 graphs, with a one-to-one spatial correspondence with this Figure.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 59

FPIR(N=1600000)

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Figure 18: Collected performance reports for algorithm A20C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 60

FPIR(N=1600000)

A30C FNIR(N=1600000, R=N, T)

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Figure 19: Collected performance reports for algorithm A30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 61

FPIR(N=1600000)

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Figure 20: Collected performance reports for algorithm A31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 62

FPIR(N=1600000)

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A32C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

● ●

●

●

● ●

●

● ●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

A32C FNIR(N, R=1, T=0)

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

A32C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

A32C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

A32C Median Duration (msec)

 10

 20

 50

 100

 200

 500

1000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

A32C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 21: Collected performance reports for algorithm A32C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 63

FPIR(N=1600000)

B30C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.4806

●

●

●●

0.4723

●

●

●●

0.4665

●

●

●●

0.4601

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

B30C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ●

0.4806

● ● ● ● 0.4723 ● ● ● ● 0.4665

● ● ● ● 0.4601

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

B30C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

● ●

●

●

● ●

● ● ● ● ● ●

●

●

● ●

●

●

● ●

●

● ●

●

● ●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

B30C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

B30C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

B30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

B30C Median Duration (msec)

 100

 200

 500

1000

2000

5000

10000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

B30C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 22: Collected performance reports for algorithm B30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 64

FPIR(N=1600000)

B31C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.3934

●

●

●●

0.3776

●

●

●●

0.367

●

●

●●

0.3566

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

B31C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

0.3934 ● ● ●

●

0.3776 ● ● ●

●

0.367 ● ● ●

●

0.3566 ● ● ●

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

B31C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

● ●

●

●

● ●

● ● ● ● ● ●

●

●

● ●

●

●

● ●

●

● ●

●

● ●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

B31C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

B31C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

B31C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

B31C Median Duration (msec)

 100

 200

 500

1000

2000

5000

10000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

B31C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 23: Collected performance reports for algorithm B31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 65

FPIR(N=1600000)

B32C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.3741

●

●

●●

0.3604

●

●

●

●

0.3528

●

●

●

●

0.346

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

B32C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ●

0.3741

● ● ● ●

0.3604

● ● ● ● 0.3528

● ● ● ● 0.346

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

B32C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

● ●

●

●

● ●

● ● ● ● ● ●

●

●

● ●

●

●

●

●

●

● ●

●

● ●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

B32C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

B32C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

B32C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

B32C Median Duration (msec)

 200

 500

1000

2000

5000

10000

20000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

B32C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 24: Collected performance reports for algorithm B32C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 66

FPIR(N=1600000)

B33C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.4167

●

●

●●

0.3902

●

●

●●

0.3754

●

●

●

●

0.3626

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

B33C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

0.4167 ● ● ●

●

0.3902 ● ● ●

●

0.3754 ● ● ●

●

0.3626 ● ● ●

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

B33C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

● ●

●

●

● ●

● ● ● ● ● ●

●

●

● ●

●

●

●

●

●

● ●

●

● ●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

B33C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

B33C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

B33C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

B33C Median Duration (msec)

 200

 500

1000

2000

5000

10000

20000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

B33C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 25: Collected performance reports for algorithm B33C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 67

FPIR(N=1600000)

C20C FNIR(N=1600000, R=N, T)

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●

18111

●

●

●●

17197

●

●

●●

16896

●

●

●●

16749 lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

C20C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ●

18111

● ● ● ●

17197

● ● ● ●

16896

● ● ●●

16749

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

C20C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 0.5

 30 100 300 1000

●

● ● ●

●

● ● ●

●● ●● ●●

●

●

● ●

●

●

● ●

● ● ●

● ● ●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

C20C FNIR(N, R=1, T=0)

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

C20C FNIR(N=1600000, R, T=0)

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

C20C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

C20C Median Duration (msec)

 200

 500

1000

2000

5000

10000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

C20C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 26: Collected performance reports for algorithm C20C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 68

FPIR(N=160000)

C30C FNIR(N=160000, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●●

●●

28325

●

●

●●

26686

●

●

●

●

26194

●

●

●

●

25859

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

C30C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

28325

● ●

26686

● ●

26194

● ●

25859

20000

160000

Num. enrolled identities, N (thousands)

C30C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

● ●

●

●

● ●

● ● ● ● ● ●

●

●

●

●

●

●

●

●

●

● ●

●

● ●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

C30C FNIR(N, R=1, T=0)

0.01

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

C30C FNIR(N=160000, R, T=0)

 0.1

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

C30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

Num. enrolled identities, N (thousands)

C30C Median Duration (msec)

 500

1000

2000

5000

10000

20000

50000

100000

 30 100 300 1000

Template Search Total

FPIR(N=160000)

C30C Selectivity(N=160000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 27: Collected performance reports for algorithm C30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 69

FPIR(N=1600000)

C31C FNIR(N=1600000, R=N, T)

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●●

●●

23160

●

●

●●

21863

●

●

●

●

21504

●

●

●

●

21264 lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

C31C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ●

23160

● ● ● ●

21863

● ● ● ●

21504

● ● ●●

21264

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

C31C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

● ●

●

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● ● ● ● ● ●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

C31C FNIR(N, R=1, T=0)

0.01

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

C31C FNIR(N=1600000, R, T=0)

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

C31C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

C31C Median Duration (msec)

 200

 500

1000

2000

5000

10000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

C31C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 28: Collected performance reports for algorithm C31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 70

FPIR(N=1600000)

C32C FNIR(N=1600000, R=N, T)

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●●

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28318

●

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26677

●

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26205

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25866 lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

C32C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ●

28318

● ● ● ●

26677

● ● ● ●

26205

● ● ●●

25866

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

C32C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 0.5

 30 100 300 1000

●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

C32C FNIR(N, R=1, T=0)

0.01

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

C32C FNIR(N=1600000, R, T=0)

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

C32C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

C32C Median Duration (msec)

 100

 200

 500

1000

2000

5000

10000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

C32C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 29: Collected performance reports for algorithm C32C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 71

FPIR(N=1600000)

D20C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

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 4180

●

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 3697

●

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 3327

●

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●

lifetime.mugshot 2999

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

D20C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

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 3697

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20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D20C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

D20C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

D20C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

D20C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D20C Median Duration (msec)

 500

1000

 30 100

Template Search Total

FPIR(N=1600000)

D20C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 30: Collected performance reports for algorithm D20C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 72

FPIR(N=1600000)

D30C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

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 3829

●

●

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●

 3436

●

●

●●

 3164 ●

●

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●

 2919 lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

D30C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

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 3829

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20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D30C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

●

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●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

D30C FNIR(N, R=1, T=0)

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

D30C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

D30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D30C Median Duration (msec)

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

D30C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 31: Collected performance reports for algorithm D30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 73

FPIR(N=1600000)

D31C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

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 4689

●

●

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●

 4184

●

●

●

●

 3819 ●

●

●

●

 3508 lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

D31C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

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20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D31C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

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lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

D31C FNIR(N, R=1, T=0)

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

D31C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

D31C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D31C Median Duration (msec)

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

D31C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 32: Collected performance reports for algorithm D31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 74

FPIR(N=1600000)

D32C FNIR(N=1600000, R=N, T)

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

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 6283

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 6122

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 5991

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lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

D32C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

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1600000

Num. enrolled identities, N (thousands)

D32C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 0.5

 30 100 300 1000

●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

D32C FNIR(N, R=1, T=0)

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

D32C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

D32C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D32C Median Duration (msec)

 50

 100

 200

 500

1000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

D32C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 33: Collected performance reports for algorithm D32C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 75

FPIR(N=1600000)

D33C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

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 3837

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 3426

●

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 3146 ●

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 2908 lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

D33C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

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20000

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Num. enrolled identities, N (thousands)

D33C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

D33C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

D33C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

D33C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D33C Median Duration (msec)

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

D33C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 34: Collected performance reports for algorithm D33C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 76

FPIR(N=1600000)

D34C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

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●

lifetime.mugshot 2936

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

D34C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

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20000

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1600000

Num. enrolled identities, N (thousands)

D34C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

D34C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

D34C FNIR(N=1600000, R, T=0)

0.05

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

D34C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

D34C Median Duration (msec)

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

D34C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 35: Collected performance reports for algorithm D34C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 77

FPIR(N=1600000)

E20C FNIR(N=1600000, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●

●

0.5392

●

●

●

●

0.5257

●

●

●

●

0.5131

●

●

●

●

lifetime.mugshot 0.4989

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

E20C FNIR(N, R=N, T)

0.02

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●

●

0.5392

●

●

●

●

0.5257 ●

●

●

●

0.5131 ●

●

●

●

0.4989

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

E20C FNIR(N, R = {1,50}, T = 0)

0.02

0.05

 0.1

 30 100 300 1000

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

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●

●

●

●

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●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

E20C FNIR(N, R=1, T=0)

0.001

0.002

0.005

0.01

0.02

0.05

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

E20C FNIR(N=1600000, R, T=0)

0.05

 0.1

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

E20C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

E20C Median Duration (msec)

 20

 50

 100

 200

 500

1000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

E20C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 36: Collected performance reports for algorithm E20C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 78

FPIR(N=1600000)

E21C FNIR(N=1600000, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●

●

0.6546

●

●

●

●

0.6381

●

●

●

●

0.6228

●

●

●

●

0.6032

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

E21C FNIR(N, R=N, T)

0.02

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ●

0.6546

● ● ● ●

0.6381

● ● ● ●

0.6228

● ● ●●

0.6032

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

E21C FNIR(N, R = {1,50}, T = 0)

0.02

0.05

 0.1

 30 100 300 1000

●

●

●

●

●

●

●

●

●

●

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●

●

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●

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●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

E21C FNIR(N, R=1, T=0)

0.001

0.002

0.005

0.01

0.02

0.05

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

E21C FNIR(N=1600000, R, T=0)

0.05

 0.1

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

E21C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

E21C Median Duration (msec)

 20

 50

 100

 200

 500

1000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

E21C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 37: Collected performance reports for algorithm E21C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 79

FPIR(N=1600000)

E30C FNIR(N=1600000, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.5343

●

●

●

●

0.5242 ●

●

●

●

0.5157 ●

●

●

●

0.5068 lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

E30C FNIR(N, R=N, T)

0.02

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●

●

0.5343 ●

●

●

●

0.5242 ●

●

●

●

0.5157 ●

●

●

●

0.5068

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

E30C FNIR(N, R = {1,50}, T = 0)

0.02

0.05

 0.1

 30 100 300 1000

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

E30C FNIR(N, R=1, T=0)

0.001

0.002

0.005

0.01

0.02

0.05

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

E30C FNIR(N=1600000, R, T=0)

0.05

 0.1

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

E30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

E30C Median Duration (msec)

 20

 50

 100

 200

 500

1000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

E30C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 38: Collected performance reports for algorithm E30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 80

FPIR(N=1600000)

E31C FNIR(N=1600000, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.535

●

●

●●

0.5254 ●

●

●

●

0.5166 ●

●

●

●

0.5072 lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

E31C FNIR(N, R=N, T)

0.02

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●

●

0.535 ●

●

●

●

0.5254 ●

●

●

●

0.5166 ●

●

●

●

0.5072

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

E31C FNIR(N, R = {1,50}, T = 0)

0.02

0.05

 0.1

 30 100 300 1000

●

●

● ●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

E31C FNIR(N, R=1, T=0)

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

E31C FNIR(N=1600000, R, T=0)

0.05

 0.1

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

E31C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

E31C Median Duration (msec)

 20

 50

 100

 200

 500

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

E31C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 39: Collected performance reports for algorithm E31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 81

FPIR(N=160000)

F20C FNIR(N=160000, R=N, T)

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

● ●

0.7617

●

●

● ●

0.7104

●

●

●

●

0.6794

●

●

●

●

0.6468

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

F20C FNIR(N, R=N, T)

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

0.7617

● ●

0.7104

● ●

0.6794

● ●

0.6468

20000

160000

Num. enrolled identities, N (thousands)

F20C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

●

●

●●

●

●

●

●

●●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

F20C FNIR(N, R=1, T=0)

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000 160000

Rank, R

F20C FNIR(N=160000, R, T=0)

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

F20C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

Num. enrolled identities, N (thousands)

F20C Median Duration (msec)

 50

 100

 200

 500

1000

2000

5000

10000

 30 100

Template Search Total

FPIR(N=160000)

F20C Selectivity(N=160000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 40: Collected performance reports for algorithm F20C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 82

FPIR(N=1600000)

F30C FNIR(N=1600000, R=N, T)

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

F30C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

●

●

0.9799

● ●

●

●

0.8987

● ●

●

●

0.7457

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

F30C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 0.5

 1

 30 100 300 1000

●

●

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●

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●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

F30C FNIR(N, R=1, T=0)

0.05

 0.1

 0.2

 0.5

 1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

F30C FNIR(N=1600000, R, T=0)

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

F30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

Num. enrolled identities, N (thousands)

F30C Median Duration (msec)

 200

 500

1000

2000

 30 100 300

Template Search Total

FPIR(N=1600000)

F30C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 41: Collected performance reports for algorithm F30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 83

FPIR(N=1600000)

F31C FNIR(N=1600000, R=N, T)

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

F31C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ●●

0.6887

● ●

●

●

0.605

● ●

●

●

0.575

● ●

●

●

0.5535

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

F31C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 0.5

 1

 30 100 300 1000

●

●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

F31C FNIR(N, R=1, T=0)

0.05

 0.1

 0.2

 0.5

 1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

F31C FNIR(N=1600000, R, T=0)

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

F31C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

Num. enrolled identities, N (thousands)

F31C Median Duration (msec)

 200

 500

1000

2000

 30 100 300

Template Search Total

FPIR(N=1600000)

F31C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 42: Collected performance reports for algorithm F31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 84

FPIR(N=640000)

G30C FNIR(N=640000, R=N, T)

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

0.9274

●

●

0.8214

●

●

0.7222

●

●

0.6022

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

G30C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

0.9274

● ●

0.8214

● ●

0.7222

● ●

0.6022

20000

160000

640000

Num. enrolled identities, N (thousands)

G30C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

●

●

●

●

●

●

●

●

●

●

● ●●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

G30C FNIR(N, R=1, T=0)

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000 160000

Rank, R

G30C FNIR(N=640000, R, T=0)

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

G30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

Num. enrolled identities, N (thousands)

G30C Median Duration (msec)

 100

 200

 500

1000

2000

 30 100 300

Template Search Total

FPIR(N=640000)

G30C Selectivity(N=640000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 43: Collected performance reports for algorithm G30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 85

FPIR(N=640000)

G31C FNIR(N=640000, R=N, T)

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

0.8029 ●

●

0.7808

●

●

0.7549

●

●

0.7119

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

G31C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

0.8029 ● ●

0.7808

● ●

0.7549

● ●

0.7119

20000

160000

640000

Num. enrolled identities, N (thousands)

G31C FNIR(N, R = {1,50}, T = 0)

 0.1

 0.2

 30 100 300 1000

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

G31C FNIR(N, R=1, T=0)

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000 160000

Rank, R

G31C FNIR(N=640000, R, T=0)

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

G31C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

Num. enrolled identities, N (thousands)

G31C Median Duration (msec)

 20

 50

 100

 200

 500

 30 100 300

Template Search Total

FPIR(N=640000)

G31C Selectivity(N=640000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 44: Collected performance reports for algorithm G31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 86

FPIR(N=640000)

H30C FNIR(N=640000, R=N, T)

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.9614 ●

●

●●

0.9452

●

●

●

●

0.9183

●

●

●

●

0.8517

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

H30C FNIR(N, R=N, T)

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ●

0.9614 ● ● ●

0.9452

● ● ●

0.9183

● ● ●

0.8517

20000

160000

640000

Num. enrolled identities, N (thousands)

H30C FNIR(N, R = {1,50}, T = 0)

 0.2

 0.5

 30 100 300 1000

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

● ●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

H30C FNIR(N, R=1, T=0)

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

Rank, R

H30C FNIR(N=640000, R, T=0)

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

H30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

Num. enrolled identities, N (thousands)

H30C Median Duration (msec)

 50

 100

 200

 500

1000

 30 100 300

Template Search Total

FPIR(N=640000)

H30C Selectivity(N=640000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 45: Collected performance reports for algorithm H30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 87

FPIR(N=1600000)

J20C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●

●

0.7739

●

●

●

●

0.6084

●

●

●

●

0.5221

●

●

●

●

0.4442

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

J20C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ●

●

●

0.6084 ● ●

●

●

0.5221 ● ●

●

●

0.4442 ● ●

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J20C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

●

●

●

●

●

●

●

●

●

● ●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

J20C FNIR(N, R=1, T=0)

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

J20C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

J20C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J20C Median Duration (msec)

 10

 20

 50

 100

 200

 500

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

J20C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 46: Collected performance reports for algorithm J20C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 88

FPIR(N=1600000)

J30C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

● ●

0.4872

●

●

●

●

0.4657

●

●

●

●

0.4539

●

●

●

●

0.4411

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

J30C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ● 0.4872

● ● ● ● 0.4657

● ● ● ● 0.4539

● ● ● ●

0.4411

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J30C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

●

●

●

●

●

●

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

J30C FNIR(N, R=1, T=0)

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

J30C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

J30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J30C Median Duration (msec)

 10

 20

 50

 100

 200

 500

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

J30C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 47: Collected performance reports for algorithm J30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 89

FPIR(N=1600000)

J31C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

● ●

0.4827

●

●

●

●

0.463

●

●

●

●

0.4522

●

●

●

●

 0.44

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

J31C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● 0.4827 ● ●

● ● 0.463 ● ●

●

0.4522 ● ● ●

● ● ● ● 0.44

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J31C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

J31C FNIR(N, R=1, T=0)

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

J31C FNIR(N=1600000, R, T=0)

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

J31C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J31C Median Duration (msec)

 10

 20

 50

 100

 200

 500

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

J31C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 48: Collected performance reports for algorithm J31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 90

FPIR(N=1600000)

J32C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.4952

●

●

● ●

0.4722

●

●

●●

0.4586

●

●

●

●

0.4442

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

J32C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ●

0.4952

● ● ● ●

0.4722

● ● ● ● 0.4586

● ● ● ●

0.4442

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J32C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 30 100 300 1000

●

●

●

●

●

●

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

J32C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

J32C FNIR(N=1600000, R, T=0)

0.05

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

J32C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J32C Median Duration (msec)

 20

 50

 100

 200

 500

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

J32C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 49: Collected performance reports for algorithm J32C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 91

FPIR(N=1600000)

J33C FNIR(N=1600000, R=N, T)

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●●

0.4971

●

●

● ●

0.4727

●

●

●●

0.4593

●

●

●●

0.4446

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

J33C FNIR(N, R=N, T)

0.05

 0.1

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ● ●

0.4971

● ● ● ●

0.4727

● ● ● ●

0.4593

● ● ● ●

0.4446

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J33C FNIR(N, R = {1,50}, T = 0)

0.02

0.05

 0.1

 0.2

 30 100 300 1000

●

●

●

●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

J33C FNIR(N, R=1, T=0)

0.002

0.005

0.01

0.02

0.05

 0.1

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

J33C FNIR(N=1600000, R, T=0)

0.05

 0.1

 0.2

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

J33C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

J33C Median Duration (msec)

 50

 100

 200

 500

1000

2000

 30 100 300 1000

Template Search Total

FPIR(N=1600000)

J33C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 50: Collected performance reports for algorithm J33C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 92

FPIR(N=160000)

L30C FNIR(N=160000, R=N, T)

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●●

● ●

 90

●

●

●●

 90

●

●

●

●

89.95

●

●

●

●

88.56

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

L30C FNIR(N, R=N, T)

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

 90 ● ●

 90

● ●

89.95

● ●

88.56

20000

160000

Num. enrolled identities, N (thousands)

L30C FNIR(N, R = {1,50}, T = 0)

 0.5

 30 100 300 1000

●

● ●

●

●

●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

L30C FNIR(N, R=1, T=0)

 0.2

 0.5

(0,1] (1,2] (2,3] (3,4] (4,300]

20000 160000

Rank, R

L30C FNIR(N=160000, R, T=0)

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

L30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

Num. enrolled identities, N (thousands)

L30C Median Duration (msec)

 500

1000

2000

5000

 30 100

Template Search Total

FPIR(N=160000)

L30C Selectivity(N=160000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 51: Collected performance reports for algorithm L30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 93

FPIR(N=160000)

L31C FNIR(N=160000, R=N, T)

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

●

●

89.98

●

●

●

●

89.75

●

●

●

●

87.18

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

L31C FNIR(N, R=N, T)

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

89.98

● ●

89.75

● ●

87.18

20000

160000

Num. enrolled identities, N (thousands)

L31C FNIR(N, R = {1,50}, T = 0)

 0.2

 0.5

 30 100 300 1000

●

● ●

●

●●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

L31C FNIR(N, R=1, T=0)

 0.2

 0.5

(0,1] (1,2] (2,3] (3,4] (4,300]

20000 160000

Rank, R

L31C FNIR(N=160000, R, T=0)

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

L31C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

Num. enrolled identities, N (thousands)

L31C Median Duration (msec)

 500

1000

2000

5000

 30 100

Template Search Total

FPIR(N=160000)

L31C Selectivity(N=160000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 52: Collected performance reports for algorithm L31C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 94

FPIR(N=640000)

M30C FNIR(N=640000, R=N, T)

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●

●

0.7328 ●

●

0.7145

●

●

0.6932

●

●

0.6547

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

M30C FNIR(N, R=N, T)

 0.2

 0.5

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

0.7328 ● ●

0.7145

● ●

0.6932

● ●

0.6547

20000

160000

640000

Num. enrolled identities, N (thousands)

M30C FNIR(N, R = {1,50}, T = 0)

 0.2

 0.5

 30 100 300 1000

●

● ●

●

●

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●

●

●

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●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

M30C FNIR(N, R=1, T=0)

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000 160000

Rank, R

M30C FNIR(N=640000, R, T=0)

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

M30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

Num. enrolled identities, N (thousands)

M30C Median Duration (msec)

 20

 50

 100

 200

 500

 30 100 300

Template Search Total

FPIR(N=640000)

M30C Selectivity(N=640000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 53: Collected performance reports for algorithm M30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 95

FPIR(N=1600000)

P30C FNIR(N=1600000, R=N, T)

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

P30C FNIR(N, R=N, T)

 0.1

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ●

●

0.9784 ● ● ●

●

 0.96

● ● ●

●

0.886

● ● ●

●

0.6935

20000

160000

640000

1600000

Num. enrolled identities, N (thousands)

P30C FNIR(N, R = {1,50}, T = 0)

0.05

 0.1

 0.2

 0.5

 30 100 300 1000

●

●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

P30C FNIR(N, R=1, T=0)

0.02

0.05

 0.1

 0.2

 0.5

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

1600000

Rank, R

P30C FNIR(N=1600000, R, T=0)

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

P30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

Num. enrolled identities, N (thousands)

P30C Median Duration (msec)

 500

1000

2000

5000

10000

 30 100 300

Template Search Total

FPIR(N=1600000)

P30C Selectivity(N=1600000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 54: Collected performance reports for algorithm P30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 96

FPIR(N=160000)

Q30C FNIR(N=160000, R=N, T)

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

●●

 8027

●

●

●●

 6749

●

●

●

●

 6367

●

●

●

●

 6074

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

Q30C FNIR(N, R=N, T)

 0.2

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

 8027

● ●

 6749

● ●

 6367

● ●

 6074

20000

160000

Num. enrolled identities, N (thousands)

Q30C FNIR(N, R = {1,50}, T = 0)

 0.2

 0.5

 30 100 300 1000

●

● ●

●

●●

●

●

●

●

●

●

lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

Q30C FNIR(N, R=1, T=0)

 0.1

 0.2

(0,1] (1,2] (2,3] (3,4] (4,300]

20000 160000

Rank, R

Q30C FNIR(N=160000, R, T=0)

 0.2

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

Q30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

Num. enrolled identities, N (thousands)

Q30C Median Duration (msec)

 100

 200

 500

1000

2000

 30 100

Template Search Total

FPIR(N=160000)

Q30C Selectivity(N=160000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 55: Collected performance reports for algorithm Q30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 97

FPIR(N=640000)

T30C FNIR(N=640000, R=N, T)

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ●

●

0.8636 ●

●

●●

0.8206

●

●

● ●

0.7346

●

●

●

●

0.4898

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

FPIR(N)

T30C FNIR(N, R=N, T)

 0.5

 1

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 0.0001

● ● ●

0.8636 ● ● ●

0.8206 ● ● ●

0.7346

● ●

●

0.4898

20000

160000

640000

Num. enrolled identities, N (thousands)

T30C FNIR(N, R = {1,50}, T = 0)

 0.5

 30 100 300 1000

●

●

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lifetime.mugshot.1

recent.mugshot.1

lifetime.webcam.1

recent.webcam.1

lifetime.mugshot.50

recent.mugshot.50

lifetime.webcam.50

recent.webcam.50

Num. enrolled images per identity

T30C FNIR(N, R=1, T=0)

 0.2

 0.5

(0,1] (1,2] (2,3] (3,4] (4,300]

20000

160000

640000

Rank, R

T30C FNIR(N=640000, R, T=0)

 0.5

 1 3 10 30

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Frac. reduction in workload by using a threshold vs. not

T30C FNIR(T) / FNIR(0)

1.1

1.2

1.3

1.4

1.5

 0.3 1

20000

160000

640000

Num. enrolled identities, N (thousands)

T30C Median Duration (msec)

 10

 20

 50

 100

 200

 500

 30 100 300

Template Search Total

FPIR(N=640000)

T30C Selectivity(N=640000)

0.0001

0.0002

0.0005

0.001

0.002

0.005

0.01

0.02

0.05

 0.1

 0.2

 0.5

 1

 2

 5

 10

 20

0.0001 0.0003 0.001 0.003 0.01 0.03 0.1 0.3 1

lifetime.mugshot

recent.mugshot

lifetime.webcam

recent.webcam

Figure 56: Collected performance reports for algorithm T30C. The figures are described at the beginning of this Appendix.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 98

A Algorithm accuracy by age group

This section details individual algorithm performance by age group as discussed in section 5.5.

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 99

False Positive Identification Rate (FPIR), A20C

False Negative Identification Rate (FNIR), A20C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

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babykidpreteenyoungparentsolder

Figure 57: The effect of age on accuracy A20C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 100

False Positive Identification Rate (FPIR), A30C

False Negative Identification Rate (FNIR), A30C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

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 3202

babykidpreteenyoungparentsolder

Figure 58: The effect of age on accuracy A30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 101

False Positive Identification Rate (FPIR), A31C

False Negative Identification Rate (FNIR), A31C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

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 3197

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 3219

babykidpreteenyoungparentsolder

Figure 59: The effect of age on accuracy A31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 102

False Positive Identification Rate (FPIR), A32C

False Negative Identification Rate (FNIR), A32C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

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babykidpreteenyoungparentsolder

Figure 60: The effect of age on accuracy A32C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 103

False Positive Identification Rate (FPIR), B30C

False Negative Identification Rate (FNIR), B30C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

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0.4708

babykidpreteenyoungparentsolder

Figure 61: The effect of age on accuracy B30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 104

False Positive Identification Rate (FPIR), B31C

False Negative Identification Rate (FNIR), B31C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

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0.3741

babykidpreteenyoungparentsolder

Figure 62: The effect of age on accuracy B31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 105

False Positive Identification Rate (FPIR), B32C

False Negative Identification Rate (FNIR), B32C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1 ●

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babykidpreteenyoungparentsolder

Figure 63: The effect of age on accuracy B32C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 106

False Positive Identification Rate (FPIR), B33C

False Negative Identification Rate (FNIR), B33C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1 ●

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babykidpreteenyoungparentsolder

Figure 64: The effect of age on accuracy B33C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 107

False Positive Identification Rate (FPIR), C20C

False Negative Identification Rate (FNIR), C20C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

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babykidpreteenyoungparentsolder

Figure 65: The effect of age on accuracy C20C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 108

False Positive Identification Rate (FPIR), C30C

False Negative Identification Rate (FNIR), C30C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

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babykidpreteenyoungparentsolder

Figure 66: The effect of age on accuracy C30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 109

False Positive Identification Rate (FPIR), C31C

False Negative Identification Rate (FNIR), C31C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

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21237

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21597

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babykidpreteenyoungparentsolder

Figure 67: The effect of age on accuracy C31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 110

False Positive Identification Rate (FPIR), C32C

False Negative Identification Rate (FNIR), C32C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

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25829

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babykidpreteenyoungparentsolder

Figure 68: The effect of age on accuracy C32C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 111

False Positive Identification Rate (FPIR), D20C

False Negative Identification Rate (FNIR), D20C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

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babykidpreteenyoungparentsolder

Figure 69: The effect of age on accuracy D20C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 112

False Positive Identification Rate (FPIR), D30C

False Negative Identification Rate (FNIR), D30C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

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 3592

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babykidpreteenyoungparentsolder

Figure 70: The effect of age on accuracy D30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 113

False Positive Identification Rate (FPIR), D31C

False Negative Identification Rate (FNIR), D31C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

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babykidpreteenyoungparentsolder

Figure 71: The effect of age on accuracy D31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 114

False Positive Identification Rate (FPIR), D32C

False Negative Identification Rate (FNIR), D32C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

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 6241

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 6302

babykidpreteenyoungparentsolder

Figure 72: The effect of age on accuracy D32C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 115

False Positive Identification Rate (FPIR), D33C

False Negative Identification Rate (FNIR), D33C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

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babykidpreteenyoungparentsolder

Figure 73: The effect of age on accuracy D33C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 116

False Positive Identification Rate (FPIR), D34C

False Negative Identification Rate (FNIR), D34C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

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 3480

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 3597

babykidpreteenyoungparentsolder

Figure 74: The effect of age on accuracy D34C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 117

False Positive Identification Rate (FPIR), E20C

False Negative Identification Rate (FNIR), E20C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

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0.5139

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●

●

●

●

●

●

0.521

●

●

●

●

●

●

●

0.5254

●

●

●

●

●

●

●

0.5299

●

●

●

●

●

●

●

0.5342

babykidpreteenyoungparentsolder

Figure 75: The effect of age on accuracy E20C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 118

False Positive Identification Rate (FPIR), E21C

False Negative Identification Rate (FNIR), E21C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.6012

●

●

●

●

●

●

●

0.6121

●

●

●

●

●

●

●

0.6212

●

●

●

●

●

●

●

0.6329

●

●

●

●

●

●

●

0.6394

●

●

●

●

●

●

●

0.6456

●

●

●

●

●

●

●

0.6513

babykidpreteenyoungparentsolder

Figure 76: The effect of age on accuracy E21C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 119

False Positive Identification Rate (FPIR), E30C

False Negative Identification Rate (FNIR), E30C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.5083 ●

●

●

●

●

●

●

0.5117

●

●

●

●

●

●

●

0.515 ●

●

●

●

●

●

●

 0.52

●

●

●

●

●

●

●

0.5232

●

●

●

●

●

●

●

0.5264

●

●

●

●

●

●

●

0.5295

babykidpreteenyoungparentsolder

Figure 77: The effect of age on accuracy E30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 120

False Positive Identification Rate (FPIR), E31C

False Negative Identification Rate (FNIR), E31C

0.0050.0020.001

0.050.020.01

 0.5 0.2 0.1

0.001

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.5085 ●

●

●

●

●

●

●

0.5119

●

●

●

●

●

●

●

0.5152 ●

●

●

●

●

●

●

0.5203

●

●

●

●

●

●

●

0.5236

●

●

●

●

●

●

●

0.5269

●

●

●

●

●

●

●

0.5301

babykidpreteenyoungparentsolder

Figure 78: The effect of age on accuracy E31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 121

False Positive Identification Rate (FPIR), F20C

False Negative Identification Rate (FNIR), F20C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

● ●

●

●

●

0.6668

●

● ● ●

● ●

●

0.6864

●

●

● ●

● ●

●

0.7011

●

●

●

●

●

●

●

0.7196

●

● ●

●

●

●

●

● ●

● ●

●

●

●

●

●

●

●

babykidpreteenyoungparentsolder

Figure 79: The effect of age on accuracy F20C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 122

False Positive Identification Rate (FPIR), F30C

False Negative Identification Rate (FNIR), F30C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

● ●

●

●

●

0.8487

●

●

●

●

●

●

●

0.9371

●

●

●

●

●

●

●

0.9809

●

●

● ●

● ● ●

0.9986

●

●

● ●

●

● ●

0.9998

●

●

● ●

● ● ●

 1

●

●

●

● ● ●

babykidpreteenyoungparentsolder

Figure 80: The effect of age on accuracy F30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 123

False Positive Identification Rate (FPIR), F31C

False Negative Identification Rate (FNIR), F31C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

● ●

0.5675

●

●

● ●

●

●

●

0.5857

●

●

● ●

●

●

●

0.6055

●

●

● ●

●

● ●

0.6409

●

●

●

●

●

● ●

0.6619

●

●

●

● ● ●

● ●

●

● ● ●

babykidpreteenyoungparentsolder

Figure 81: The effect of age on accuracy F31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 124

False Positive Identification Rate (FPIR), G30C

False Negative Identification Rate (FNIR), G30C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

● ● ●

●

●

●

●

0.6717

●

●

● ●

●

●

●

0.7712

●

●

● ●

●

●

●

0.8487

● ●

● ●

●

● ●

0.931

●

● ●

●

● ●

●

● ●

● ●

●

●

● ●

● ●

●

babykidpreteenyoungparentsolder

Figure 82: The effect of age on accuracy G30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 125

False Positive Identification Rate (FPIR), G31C

False Negative Identification Rate (FNIR), G31C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.7412

●

●

● ●

●

● ●

0.7657

●

●

● ●

●

● ●

0.785

●

● ●

● ●

●

●

● ●

● ●

●

●

● ●

● ●

●

●

● ●

●

●

●

babykidpreteenyoungparentsolder

Figure 83: The effect of age on accuracy G31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 126

False Positive Identification Rate (FPIR), H30C

False Negative Identification Rate (FNIR), H30C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.8354

●

●

●

●

●

●

●

0.8717

●

● ●

●

●

●

●

0.894

●

●

●

●

●

●

●

0.9183

●

●

●

●

●

●

●

0.9292

●

●

●

●

●

●

●

●

●

●

●

●

babykidpreteenyoungparentsolder

Figure 84: The effect of age on accuracy H30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 127

False Positive Identification Rate (FPIR), J20C

False Negative Identification Rate (FNIR), J20C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.3936

●

●

●

●

●

●

●

0.4184

●

●

●

●

● ●

●

0.4408

●

●

●

●

●

●

●

0.4753

●

●

●

●

●

●

●

0.4969

●

●

●

●

●

●

●

0.5188

●

●

●

●

●

●

babykidpreteenyoungparentsolder

Figure 85: The effect of age on accuracy J20C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 128

False Positive Identification Rate (FPIR), J30C

False Negative Identification Rate (FNIR), J30C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

● ●

●

0.4349

●

●

●

●

●

●

●

0.4408

●

●

●

●

●

●

●

0.4463

●

●

●

●

●

●

●

0.4547

●

●

●

●

●

●

●

0.4595

●

●

●

●

●

●

●

0.4641

●

●

●

●

●

●

babykidpreteenyoungparentsolder

Figure 86: The effect of age on accuracy J30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 129

False Positive Identification Rate (FPIR), J31C

False Negative Identification Rate (FNIR), J31C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

● ●

●

0.4337

●

●

●

●

●

●

●

0.4394

●

●

●

●

●

●

●

0.4445

●

●

●

●

●

●

●

0.4524

●

●

●

●

●

●

●

0.457

●

●

●

●

●

●

●

0.4612

●

●

●

●

●

●

babykidpreteenyoungparentsolder

Figure 87: The effect of age on accuracy J31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 130

False Positive Identification Rate (FPIR), J32C

False Negative Identification Rate (FNIR), J32C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.437

●

●

●

●

●

●

●

0.4435

●

●

●

●

●

●

●

0.4494

●

●

●

●

●

●

●

0.4578

●

●

●

●

●

●

●

0.463

●

●

●

●

●

●

●

0.4683

●

●

●

●

●

●

babykidpreteenyoungparentsolder

Figure 88: The effect of age on accuracy J32C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 131

False Positive Identification Rate (FPIR), J33C

False Negative Identification Rate (FNIR), J33C

 0.5 0.2 0.1

0.050.020.01

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

●

●

0.438

●

●

●

●

● ●

●

0.4446

●

●

●

●

●

●

●

0.4506

●

●

●

●

●

●

●

0.4594

●

●

●

●

●

●

●

0.4646

●

●

●

●

●

●

●

0.4695

●

●

●

●

●

●

●

0.4745

babykidpreteenyoungparentsolder

Figure 89: The effect of age on accuracy J33C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 132

False Positive Identification Rate (FPIR), L30C

False Negative Identification Rate (FNIR), L30C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

● ● ●

85.38

●

●

●

●

●

●

●

88.02

●

●

●

●

●

●

●

89.08

●

●

●

●

●

●

●

 89.7

●

●

●

●

●

●

●

89.85

●

●

●

●

●

●

●

89.92

●

●

●

●

● ●

●

89.96

babykidpreteenyoungparentsolder

Figure 90: The effect of age on accuracy L30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 133

False Positive Identification Rate (FPIR), L31C

False Negative Identification Rate (FNIR), L31C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

●

●

●

●

●

● ●

84.19

●

●

●

●

●

●

●

87.19

●

●

●

●

●

●

●

88.56

●

●

●

●

●

●

●

89.47

●

●

●

●

●

●

●

89.72

●

●

●

●

●

● ●

89.85

●

●

●

●

●

●

●

89.92

babykidpreteenyoungparentsolder

Figure 91: The effect of age on accuracy L31C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 134

False Positive Identification Rate (FPIR), M30C

False Negative Identification Rate (FNIR), M30C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

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0.6479

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0.6637

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0.6768

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0.6961

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0.7075

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babykidpreteenyoungparentsolder

Figure 92: The effect of age on accuracy M30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 135

False Positive Identification Rate (FPIR), Q30C

False Negative Identification Rate (FNIR), Q30C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

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 6133

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 6271

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 6385

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 6545

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 6638

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babykidpreteenyoungparentsolder

Figure 93: The effect of age on accuracy Q30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 136

False Positive Identification Rate (FPIR), S20C

False Negative Identification Rate (FNIR), S20C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

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0.7724

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0.7867

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0.7974

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0.8108

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0.818

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0.8241

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0.8295

babykidpreteenyoungparentsolder

Figure 94: The effect of age on accuracy S20C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”
MAY 26, 2014 FRVT - FACE RECOGNITION TECHNOLOGY EVALUATION 137

False Positive Identification Rate (FPIR), T30C

False Negative Identification Rate (FNIR), T30C

 0.5 0.2 0.1

0.05

0.001 0.003 0.01 0.03 0.1 0.3 1

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0.7588

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0.7842

babykidpreteenyoungparentsolder

Figure 95: The effect of age on accuracy T30C. Each trace is a detection error tradeoff characteristic for individuals within one of seven age groups at the time theirsearch image was collected. Upward vertical displacement indicates increased difficult for persons to be recognized as themselves. Rightward displacement indicates thatindividuals are more often confused a somebody else (i.e. false positives).

A = 3M/Cogent B = Cognitec C = Neurotechnology D = Safran Morpho E = NEC F = Tsinghua U.

G = Hisign H = CAS-IA I = CAS-ICT J = Toshiba L = Tsinghua U. II M = HP

P = Zhuhai-Yisheng Q = JunYu S = Decatur T = Ayonix

FNIR(N,R,T,L) “Miss rate”

FPIR(N,T,L) “False alarm rate”