NISTIR 8052

Face Recognition Vendor Test (FRVT) 

Performance of Automated Gender Classification 

Algorithms 

Mei Ngan 

Patrick Grother 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.IR.8052 
 

 

 

 

 

NISTIR 8052 

Face Recognition Vendor Test (FRVT) 

Performance of Automated Gender Classification 

Algorithms 

Mei Ngan 

Patrick Grother 

Information Access Division 

Information Technology Laboratory 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.IR.8052 

April 2015 

U.S. Department of Commerce 

Penny Pritzker, Secretary 

National Institute of Standards and Technology 

Willie May, Acting Under Secretary of Commerce for Standards and Technology and Acting Director 
Face Recognition Vendor Test (FRVT)
 

Performance of Automated Gender Classification Algorithms

NIST Interagency Report 8052

Mei Ngan and Patrick Grother

Information Access Division
 

National Institute of Standards and Technology
 
FRVT i

Executive Summary
 

Introduction

Facial gender classification is an area studied in the Face Recognition Vendor Test (FRVT) Still Facial Images Track. While

peripheral to automated face recognition, it has become a growing area of research, with potential use in various applications. The motivation for gender classification systems has grown in recent years, with rise of the digital age and the

increase in human-computer interaction. Gender-based indexing of face images, gender-targeted surveillance (e.g., monitoring gender-restricted areas), gender-adaptive targeted marketing (e.g., displaying gender-specific advertisements from

digital signage), and passive gender demographic data collection are potential applications of automated gender classification.

NIST performed a large scale empirical evaluation of facial gender classification algorithms, with participation from five

commercial providers and one university, using large operational datasets comprised of facial images from visas and law

enforcement mugshots, leveraging a combined corpus of close to 1 million images. NIST employed a lights-out, black-box

testing methodology designed to model operational reality where software is shipped and used ”as-is” without subsequent algorithmic training. Core gender classification accuracy was baselined over a large dataset composed of images

collected under well-controlled pose, illumination, and facial expression conditions, then assessed demographically by

gender, age group, and ethnicity. Analysis on commonly benchmarked ”in the wild” (i.e., unconstrained) datasets was

conducted and compared with those from the constrained dataset. The impact of number of image samples per subject was

captured and assessments of classification performance on sketches and gender verification accuracy were documented.

Key Results

Core Accuracy and Speed: Gender classification accuracy depends strongly on the provider of the core technology.

Broadly, there is a threefold difference between the most accurate and the least accurate algorithm in terms of gender

classification error, which is the percentage of images classified incorrectly. The most accurate algorithm (E32D from

NEC) can correctly classify the gender of a person over a constrained database of approximately 1 million images 96.5% of

the time. All algorithms can perform gender classification on a single image in less than 0.25 seconds with one server-class

processor. The most accurate algorithm, on average, performs classification in 0.04 seconds. Section 3.1.

Impact of Demographic Data on Accuracy: For a dataset of 240 thousand visa images, it is empirically observed that,

overall, gender classification is more accurate in males than females. All of the algorithms show a significant decrease in

gender classification accuracy as age increases for adult females, with an empirically decreasing trend in accuracy seen in

females past age 50. Gender classification is more accurate in adult males (ages 21-60) than young boys (ages 0-10) for all

of the algorithms. For females, gender classification is the most accurate in young adults (ages 21-30). A majority of the

algorithms demonstrate the lowest gender classification accuracy on subjects from Taiwan and Japan, with males being

more often misclassified than females. Section 3.2 and 3.3.

These results state empirical observations for the particular dataset, but they do not determine cause. The impact of factors

potentially driving the observed results between age and ethnicity, such as facial features innate to certain ethnic groups,

facial changes with age, and hairstyles are not studied in this report. Further research would be required to objectively

verify these conjectures.

Gender Verification Accuracy: In addition to a binary male or female decision, algorithms provide a real-valued score

of maleness-femaleness. This score can be used to tradeoff the type 1 error versus type 2 error. For a system with an

objective to verify that a person is female (e.g., in gender-restricted scenarios), to ensure that 99% of the time, the subject

is indeed a female would result in 10% of the true female population being falsely classified as males, or inconveniently

rejected as being female. The same Detection Error Tradeoff (DET) analysis can be done by gender and for different verification thresholds to support specific applications. Section 3.7.1.

Impact of Number of Image Samples on Accuracy: The FRVT Application Programming Interface (API) [12] supports

multiple still image input to the algorithm software for gender classification, which enables the analysis of gender classification performance versus the number of image samples of the same person. For contemporaneous mugshot images of the

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
FRVT ii

same subject collected within a one year period, the results show gender classification accuracy monotonically increasing

as the number of image samples provided increased for most of the algorithms. Misclassification rates drop by as much as

50% between one and four input images, depending on the algorithm. Gender classification times increase linearly with

respect to the number of image samples, which is the expected behavior. Section 3.4.

Comparison against Academic Methods: A performance evaluation was done with two commonly benchmarked in

the wild datasets composed of face images that show a large range of variation in factors such as pose, illumination, facial expression, and background. The study attempts to compare FRVT gender classification participants with published

methods from the academic literature. The top performing FRVT participants achieved comparable or higher overall gender classification accuracy when compared to the published methods that tested their algorithms with the same number

of images for both datasets.

Straight comparison of performance results between the FRVT participants and published methods remains to be a challenge given some methods are tested on only a subset of the images and others use different testing protocols, which in all

cases, allowed the implementation to train on a set of images through every iteration of testing. For the FRVT participant

results documented, all images from the datasets were used for testing, and NIST employed a lights-out, black-box testing

methodology that did not involve any type of algorithm training during evaluation. This is designed to model operational

reality where software is shipped and used as-is without algorithmic training. Section 3.5.

Constrained versus In the Wild: Comparison of algorithmic performance on constrained versus in the wild images indicate that the qualities of in the wild images (e.g., variations in pose, illumination, facial expression, and background) does

have a negative impact on algorithm certainty of gender classification. Greater average gender classification certainty

differences are observed in males between constrained and in the wild images. Section 3.6.

Sketches: By running the algorithms through a set of 856 non-operational sketch images that were derived from a set

of photographs, the most accurate algorithm (B31D from Cognitec) achieves an overall classification accuracy of 93.8%.

An important caveat is that gender classification on sketches was never declared to be part of this FRVT study and better

algorithms may be available from the providers. That said, automated face recognition algorithms are being used to recognize sketches operationally, and gender classification on sketches may help reduce the search space for face recognition

in those scenarios. Section 3.7.2.

Caveats

Nature of the data: The main dataset used for overall accuracy assessment is comprised of close to 1 million images collected under well-controlled pose, illumination, and facial expression conditions. Although image collection was subject

to the guidelines published by the Department of State (DoS) [3] and the Federal Bureau of Investigation (FBI), the images

are compressed JPEG files which exhibit artifacts of JPEG compression causing reduction in image detail. With more detail

available in less compressed images, gender classification accuracy may improve, but errors will still likely exist due to

variation driven by intrinsic and extrinsic factors.

Failure to compute: The results presented in this document are for cases where gender classification computation did

not fail. That is, metrics did not include a penalty for cases where algorithms failed to generate a gender classification

decision and score. As such, the algorithms were evaluated based on the goodness of the results that they were able to

generate, which might make an algorithm with a high failure-to-compute or no-attempt-to-compute rate seem to be more

effective than a robust algorithm that has a zero failure-to-compute rate.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
FRVT	 iii

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

(FBI) and the Science and Technology (S&T) Directorate in the Department of Homeland Security (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.

Release Notes

.	 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 Hadley Wickham’s ggplot2 [27] package running under , the capabilities of which extend beyond those evident in this document.

. Contact: Correspondence regarding this report should be directed to FRVT2012 at NIST dot GOV.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
CONTENTS FRVT iv
 

Contents
 

EXECUTIVE SUMMARY I
 

CAVEATS II
 

ACKNOWLEDGEMENTS III
 

DISCLAIMER III
 

RELEASE NOTES III
 

1 INTRODUCTION 1
 

1.1 PURPOSE ............................................................. 1
 

1.2 APPLICATION SCENARIOS .................................................... 1
 

2 METHODOLOGY 1
 

2.1 TEST ENVIRONMENT ...................................................... 1
 

2.2 ALGORITHMS .......................................................... 2
 

2.3 IMAGE DATASET ......................................................... 2
 

2.4 PERFORMANCE METRICS .................................................... 4
 

2.4.1 CLASSIFICATION ACCURACY .............................................. 4
 

2.4.2 GENDER VERIFICATION ERROR ............................................. 4
 

3 RESULTS 4
 

3.1 LARGE CONSTRAINED DATASET ................................................ 4
 

3.1.1 ACCURACY ........................................................ 4
 

3.1.2 SPEED .......................................................... 5
 

3.1.3 FAILURE TO COMPUTE RATE .............................................. 6
 

3.2 AGE ............................................................... 7
 

3.3 ETHNICITY ............................................................ 9
 

3.4 MULTIPLE IMAGE SAMPLES ................................................... 11
 

3.5 IN THE WILD .......................................................... 12
 

3.6 CONSTRAINED VERSUS IN THE WILD .............................................. 14
 

3.7 SPECIFIC APPLICATIONS .................................................... 15
 

3.7.1 GENDER VERIFICATION ................................................. 15
 

3.7.2 SKETCHES ........................................................ 15
 

List of Figures

1 CLASSIFICATION ACCURACY OVER LARGE CONSTRAINED DATASET ............................. 5
 

2 GENDER CLASSIFICATION TIMING ............................................... 6
 

3 CLASSIFICATION ACCURACY BY AGE GROUP ......................................... 8
 

4 EXAMPLES OF MISCLASSIFICATIONS IN FEMALES ABOVE AGE 50 ............................... 9
 

5 MALENESS-FEMALENESS VALUE DISTRIBUTION BY ETHNICITY ................................ 10
 

6 CLASSIFICATION ACCURACY BY NUMBER OF IMAGE SAMPLES ................................ 11
 

7 TIMING BY NUMBER OF IMAGE SAMPLES ............................................ 12
 

8 EXAMPLES OF CONSTRAINED AND IN THE WILD IMAGES ................................... 14
 

9 MALENESS-FEMALENESS VALUES (CONSTRAINED VERSUS IN THE WILD) ........................... 14
 

10 GENDER VERIFICATION ACCURACY .............................................. 15
 

11 EXAMPLES OF FERET IMAGES AND SKETCHES ......................................... 16
 

List of Tables

1 PARTICIPANTS .......................................................... 2
 

2 IMAGE DATASET DESCRIPTIONS ................................................. 3
 

3 IMAGE DATASET SIZES ...................................................... 3
 

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
LIST OF TABLES FRVT v

4 FAILURE TO COMPUTE RATES .................................................. 7
 

5 CLASSIFICATION ACCURACY BY AGE GROUP .......................................... 8
 

6 ACCURACY BY ETHNIC PROXY GROUP ............................................. 9
 

7 CLASSIFICATION ACCURACY ON GROUPS DATASET ..................................... 13
 

8 CLASSIFICATION ACCURACY ON LFW DATASET ........................................ 13
 

9 AVERAGE MALENESS-FEMALENESS VALUES (CONSTRAINED VERSUS IN THE WILD) ..................... 14
 

10 ACCURACY ON SKETCHES .................................................... 16
 

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
FRVT 1

1 Introduction

1.1 Purpose

Gender classification of a face in one or more images is an area investigated in the Face Recognition Vendor Test (FRVT)

with Still Images Track. Similar to age, gender is viewed as a soft biometric trait [17], and its automated characterization

has become a growing area of study that has applications in surveillance, human-computerinteraction, and image retrieval

systems. Some of the earliest attempts to perform gender classification using computer vision techniques occurred over

two decades ago, and a number of methods have been published in the literature over the years addressing the problem

of gender classification using facial images. In recent years, gender classification on ”in the wild” (i.e., unconstrained)

images has been investigated given an increased attention to face recognition in the wild and commercial applications of

gender classification.

The main goals of this evaluation are to:

- Provide an objective, independent, open, and free assessment of current automated gender classification technology.

- Leverage massive operational corpora. The availability of images from large populations (around one million) supports statistical significance and repeatability of the studies. The use of operational images brings greater operational

relevance to the test results.

- Investigate gender classification accuracy across various factors, including age, ethnicity, and constrained versus in

the wild facial images.

1.2 Application Scenarios

The motivation for gender classification has grown in the last few decades given the rise of the digital age and the increase in human-computer interaction. The process of gender determination has potential application in at least the areas

described below:

Gender as criterion for indexing into large-scale biometric databases for faster retrieval has been discussed [26] and can

also apply to automatic sorting and image retrieval from digital photo albums and the internet. For example, gender can

be used for one-to-many search partitioning where given thresholds for male and female scores that represent a certain

level of gender classification certainty, a schema can be employed where if a score falls within the certainty threshold, it

performs a search only on the partition specific to that gender, thereby reducing the search space. For gender scores that

don’t fall within a particular gender certainty threshold (representing less certainty in gender classification), the entire

database can be searched.

Gender-targeted surveillance can assist with monitoring gender-restricted areas and elevated threat levels that might be

associated with a specific gender. [18]
 

Gender-adaptive human-computer interaction is on the rise given the popularity of digital signage and the opportunity
 

for targeted digital marketing. Targeted advertisements can be displayed based on the gender of the audience walking
 

past a digital sign.
 

Passive gender demographic data collection [16] can be achieved and used to support decisions relating to, for example,

the ratio of female versus male product offerings in a store.

2 Methodology

2.1 Test Environment

The evaluation was conducted offline at a NIST facility. Offline evaluations are attractive because they allow uniform,

fair, repeatable, and large-scale statistically robust testing. However, they do not capture all aspects of an operational

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
2.2 ALGORITHMS FRVT 2

system. While this evaluation is designed to mimic operational reality as much as possible, it does not include a live

image acquisition component or any interaction with real users. Testing was performed on high-end server-class blades

running the Linux operating system. Most of the blades were 12-core machines with dual processors running at 3.47 GHz

with 192 GB of main memory. The test harness used concurrent processing to distribute workload across dozens of blades.

2.2 Algorithms

The FRVT program was open to participation worldwide. The participation window opened on July 25, 2012, and submission to the final phase for gender classification algorithms closed on October 4, 2013. There was no charge to participate.

The process and format of algorithm submissions to NIST was described in the FRVT Still Face Image and Video Concept,

Evaluation Plan and Application Programming Interface (API) document [12]. Participants provided their submissions

in the form of libraries compiled on a specified Linux kernel, which were linked against NIST’s test harness to produce

executables. NIST provided a validation package to participants to ensure that NIST’s execution of submitted libraries

produced the expected output on NIST’s test machines.

FRVT had three submission phases where participants could submit algorithms to NIST. This report documents the results

of all algorithms submitted in the final phase or the most recent submission for participants who only submitted in prior

phases.

Table 1 lists the FRVT participants who submitted algorithms for gender classification, and the alphanumeric code associated with each of their submissions. For each participant, the algorithms are labeled numerically by chronological order

of submission. The letter codes assigned to the participants are also located at the bottom of each page for reference.

Participant Letter Code Submissions

Aug. 2012 Mar. 2013 Oct. 2013

Cognitec B B10D B20D B30D,B31D

Neurotechnology C C30D

NEC E E10D E30D,E31D,E32D

Tsinghua University F F10D F30D

MITRE K K10D

Zhuhai-Yisheng P P30D

Table 1: FRVT Gender Classification Participants

2.3 Image Dataset

This report documents the use of the following datasets1:

- Mugshot images: This dataset consists of facial images collected by various law enforcement (LEO) agencies and

transmitted to the FBI as part of various criminal record checks.

- Visa images: This dataset consists of facial images for visa applicants.

- LFW: This is a public dataset composed of unconstrained facial images collected from the web [15].

- GROUPS: This is a public dataset, which is a collection of unconstrained images of groups of people from Flickr [10].

- FERET Sketch images: The FERET [20] database was collected in the 1990s and has been very widely studied. The

City University of Hong Kong employed an artist to produce, for each person, ”a face photo with lighting variation

1Operational datasets used in this study were shared with NIST only for use in biometric technology evaluations under agreements in which biometric

samples were anonymously coded by the provider; code translations were never shared with NIST; and no personally identifiable information (PII)

beyond the biometric sample was shared with NIST.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
2.3 IMAGE DATASET FRVT 3

and a sketch with shape exaggeration drawn by an artist when viewing this photo”, published as the CUHK Face

Sketch FERET Database (CUFSF) [25].

The dataset properties are summarized in Table 2.

Property MUGSHOT VISA LFW GROUPS CUFSF

Collection

Environment

Law enforcement

booking

Visa application

process

Internet images Internet images Sketches

Collection Era  ̃1960s-2008  ̃1996-2010 Unknown Unknown

Digital, Paper

Scan

Digital, few paper Mostly digital Unknown Unknown Paper Scan

Documentation See NIST Special

Database 32 [2]

[3] See [15] See [10] See [25]

Image size Various, 480x600,

768x960

Most 252x300 250x250

Compression JPEG  ̃20:1 JPEG, mean size:

16.2kB

JPEG JPEG JPEG

Eye to eye

distance

Mean = 108

pixels, SD = 40

pixels

Median = 71

pixels

Median = 18.5

pixels

Frontal pose Moderate control.

Known profile

images excluded.

Well controlled Uncontrolled Uncontrolled

Full frontal Mostly not. Yes, in most

geometry Varying amounts

of the torso are

visible.

cases. Faces are

more cropped

(i.e., smaller

background) than

ISO2 Full Frontal

requires.

Source Operational data Operational data Public dataset Public dataset Public dataset

Table 2: Image dataset descriptions.

The operational datasets are characterized by population sizes well in excess of all published gender classification tests.

The number of images are given in Table 3.

Quantity MUGSHOT VISA LFW GROUPS CUFSF

Number of 587 680 364 086 13 233 28 231 856

gender labeled

face images

Gender Male: 293 840 Male: 185 164 Male: 10 256 Male: 13 672 Male: 494

Female: 293 840 Female: 178 922 Female: 2 977 Female: 14 559 Female: 363

Table 3: Image dataset sizes.

2The International Organization of Standardization, (ISO), is an international standard-setting body composed of representatives from various national

standards organizations.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
2.4 PERFORMANCE METRICS FRVT 4

2.4 Performance Metrics

The following performance measures will be reported in the assessment of gender classification:

2.4.1 Classification Accuracy

Male Accuracy is defined as the number of correctly classified male images, TM, divided by the total number of male

images, M. i.e.,

Male accuracy = TM (1) M

Female Accuracy is defined as the number of correctly classified female images, T F, divided by the total number of female

images, F. i.e.,

Female accuracy = T F (2) F

Overall accuracy is defined as the sum of correctly classified male and female images divided by the total number of

images. i.e.,

TM + T F Overall accuracy = (3) M + F

2.4.2 Gender Verification Error

Per the FRVT API [12], a real-valued measure of maleness-femaleness on [0,1] is provided by the gender classification

algorithms, with a value of 0 indicating certainty that the subject is a male and 1 indicating certainty that the subject is

a female. The maleness-femaleness values can be plotted as a Detection Error Tradeoff (DET) characteristic, where, for

example, in a gender-based access control scenario that only allows female entry, the rate of males being granted access

(i.e., the false female rate) is traded off against the rate of females being denied access (i.e., the false male rate).

For gender verification, the fundamental error rates are defined as:

Number of females with a maleness-femaleness value < threshold, T False male rate(T) = (4) Number of females

Number of males with a maleness-femaleness value  threshold, T False female rate(T) = (5) Number of males

3 Results

3.1 Gender Classification on Large Constrained Dataset

3.1.1 Accuracy

Gender classification accuracy was baselined against a large dataset composed of visa and mugshot images collected under

well-controlled pose, illumination, and facial expression conditions. The dataset consisted of 951 766 images (472 762

females and 479 004 males) with a relatively balanced number of females and males. Gender classification performance

for each algorithm is presented in Table 1a. Per the FRVT API [12], in addition to providing a binary male or female

classification, the gender classification algorithms were asked to provide a real-valued measure of maleness-femaleness

on [0,1], where a value of 0 indicates certainty that the subject is a male and 1 indicates certainty that the subject is

a female. Figure 1b plots the distribution of maleness-femaleness values for the algorithms that provided continuous

maleness-femaleness values.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.1 LARGE CONSTRAINED DATASET FRVT	 5

C30D 

F M Female Male Overall 

Accuracy (%) Accuracy (%) Accuracy (%) B30D B31D

8

6 6

# Images 472762 479004 951766 6

4 4

4 

B30D 88.8 97.6 93.2 2 2 2 

0 0 0 B31D 88.7 97.9 93.3 E30D E31D 

30 30 

E32D 

C30D 88.7 95.0 91.9 20 

20 20

Density

E30D 91.0 97.3 94.2 10 10 10 

E31D 91.9 97.2 94.5 0 0 0 

P30D 

3 E32D 95.6 97.5 96.5

2 

F30D 87.7 89.5 88.6

1 

K10D 88.6 93.0 90.8

0 

P30D 81.9 94.4 88.1

0.000.250.500.751.00 

Maleness−Femaleness Value 

(a) Classification accuracy	 (b) Distribution of maleness-femaleness value

Figure 1: Table summarizing gender classification accuracy and density plots showing distribution of maleness-femaleness value. Table

and plots were generated with 951 766 images.

Results and notable observations:

•	 The algorithm with the highest overall accuracy (E32D) can correctly classify gender 96.5% of the time over a dataset

of 951 766 images.

•	 For males, classification performance is closer between the algorithms, with participants B and E being the top

performers, achieving accuracies of 97.9% (B31D) and 97.5% (E32D) respectively. For females, the leading participant

(E32D) achieves accuracy of 95.6%, with the next most accurate participant (B30D) being 6.8% lower in accuracy for

the same gender class.

•	 Gender classification accuracy is empirically lower in females than males for all of the algorithms. Accuracy in

females is 1.8% to 12.5% lower than males, depending on the algorithm. This could be a result of higher female

misclassification rates observed in certain age groups, which is discussed in Section 3.2.

•	 The distribution of the maleness-femaleness value for most of the algorithms shows clear separation of male and

female classification. The higher misclassification rate seen in females is also supported in the density plots where

overlapping past the implied male threshold value of 0.5 or less is visible. Algorithms F30D and K10D did not

provide continuous values for maleness-femaleness.

3.1.2 Speed

Speed could be an important performance factor in some gender classification applications where there exists a limited

window of time for a decision based on the outcome, such as when a person walks past a digital sign. The use of gender as

criterion for indexing into large-scale biometric databases would levy rapid speed requirements on gender classification

algorithms to make it operationally viable.

Figure 2 presents the distribution of gender classification times for each algorithm. Gender classification time is the amount

of time elapsed computing the gender from pixel data of a face image. It does not include any pre-processing steps

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.1 LARGE CONSTRAINED DATASET FRVT	 6

performed by the test software such as loading the image from disk or extracting image data from a compressed JPEG file.

The timing machine was a server-class blade with a CPU running at 3.47 GHz. For more details on the testing environment,

see Section 2.1.

Accuracy = 93.2% 

Accuracy = 93.3% 

Accuracy = 91.9% 

Accuracy = 94.2% 

Accuracy = 94.5% 

Accuracy = 96.5% 

Accuracy = 88.6% 

Accuracy = 90.8% 

Accuracy = 88.1% 

B30D 

B31D 

C30D 

E30D 

E31D 

E32D 

F30D 

K10D 

P30D 

0.00 0.05 0.10 0.15 0.20 0.25 0.30 

Algorithm 

Gender Classification Time (sec) 

Figure 2: Boxplots of the distribution of gender classification times. Plots were generated over 5 000 gender estimates. For reference,

overall classification accuracy against a population of 951 766 is reported on the right.

Results and notable observations:

•	 Gender classification time varies considerably from one participant to another. K10D can perform classification in

less than 0.025 seconds while B31D takes about nine times longer than that, on average.

•	 No clear speed-accuracy tradeoff exists between the gender classification algorithms. While K10D has the fastest

classification speeds, it is not the lowest in accuracy, and while participant B’s algorithms are the slowest, they do

not achieve the highest accuracy. Partipant E’s algorithms are among the lowest in classification times, and they also

attain the highest overall classification accuracy.

•	 Participant E appears to have fixed gender classification times, while exhibiting evident variance in accuracy between its algorithms. Participant B shows observable differences in classification times between its two algorithms

with nominal changes in accuracy.

3.1.3 Failure to Compute Rate

The accuracy results presented above were computed for cases where gender classification did not fail. The error metrics

do not include a penalty for cases where an algorithm failed to generate a gender estimate. Per the FRVT API [12], a

failure to compute occurs when an algorithm’s code returns a non-zero return value from a call to its gender classification

function, and hence fails to generate an gender estimate. This can be a result of software issues (e.g., memory corruption),

algorithmic limitations (e.g., failure to find eyes in small images), elective refusal to process the input (e.g., image is

assessed to have insufficient quality), or specific vendor-defined failures. Table 4 presents the fraction of images for which

algorithms failed to generate a gender estimate over 951 766 images.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.2 AGE FRVT 7

Num Images B30D B31D E30D E31D E32D K10D P30D 

951766 0.0011 0.0011 0.0063 0.0063 0.0063 0.1554 0.0031

Table 4: Table summarizing failure to compute ratio over 951 766 images.

Results and notable observations:

•	 Algorithms C30D and F30D did not produce any failures to generate a gender estimate on the dataset of 951 766

images.

•	 Eight out of nine of the algorithms have insignificant failure to compute ratios over the massive number of images

processed. While the dataset used is comprised of visa and mugshot images collected under published collection

guidelines, the existence of a small number of bad images is inevitable given the operational nature of the data.

Issues with images included occlusion, closed eyes, and pathological quality.

•	 Participant K fails on approximately 15% of the images, with the reason being ”involuntary failure to extract features

from the image” (as indicated in the FRVT API).

3.2 Age

Age is a demographic trait that can impact gender perception. Studies from the anthropological literature have indicated

that there are a number of skeletal structure differences between the faces of male and female adults. In contrast, claims

have been made that the faces of boys and girls are very similar in skeletal facial structure, posing a challenge to gender

classification in children [5,8]. Studies have also examined the impact of a person’s age on gender classification accuracy on

limited amounts of data [13]. The dataset used in this experiment contains a large number of gender-labeled visa images

collected under well-controlled pose, illumination, and facial expression conditions. The dataset contains a balanced

number of males and females over each of the age ranges. Gender classification accuracy between males and females

across age ranges is assessed and summarized in Figure 3 and Table 5.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
10

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11−20 21−30 31−40 41−51−60 61−70 71−80 81−90 

Age Range (years)	 Age Range (years) 

(a) By algorithm	 (b) By gender

Figure 3: Line plots showing the classification accuracy over age ranges (a) by algorithm and (b) by gender. Plots were generated with

243 023 visa images.

Age Range # Females # Males B30D B31D C30D E30D E31D E32D F30D K10D P30D 

0−10 11141 11442 77.0 | 82.8 77.1 | 82.6 90.2 | 20.3 68.1 | 72.9 68.5 | 74.1 68.0 | 72.1 78.0 | 34.0 87.8 | 25.3 93.6 | 16.3 

11−20 11067 10859 92.9 | 92.2 93.7 | 91.3 97.5 | 74.0 94.2 | 90.2 92.5 | 91.9 94.7 | 90.7 88.3 | 72.2 91.3 | 75.4 91.4 | 81.9 

21−30 32966 36786 97.3 | 97.9 97.5 | 97.9 98.5 | 93.5 96.4 | 98.3 95.1 | 98.7 97.3 | 98.7 92.2 | 79.4 90.8 | 93.2 94.6 | 95.9 

31−40 21848 27185 96.0 | 99.0 96.0 | 99.1 97.9 | 94.4 95.0 | 98.7 93.5 | 98.9 97.0 | 98.9 90.9 | 80.5 87.0 | 95.1 92.6 | 96.9 

41−50 16330 18145 92.2 | 99.4 92.5 | 99.5 95.8 | 94.3 92.0 | 98.9 90.0 | 98.9 96.4 | 98.8 88.5 | 78.9 82.8 | 95.4 87.2 | 97.3 

51−60 14376 12185 84.4 | 99.5 85.4 | 99.6 92.4 | 93.7 87.4 | 98.9 85.0 | 98.8 94.7 | 98.4 84.9 | 78.2 77.9 | 95.0 78.6 | 97.8 

61−70 7320 5960 74.4 | 99.4 75.1 | 99.5 86.4 | 93.1 79.7 | 98.5 78.2 | 98.3 90.7 | 97.4 77.7 | 78.9 67.7 | 94.5 66.4 | 97.6 

71−80 2579 1960 59.3 | 99.5 59.9 | 99.4 73.2 | 92.2 67.4 | 97.1 68.3 | 97.1 84.1 | 97.0 64.7 | 79.3 55.6 | 93.6 53.8 | 95.3 

81−90 457 355 49.2 | 97.6 49.7 | 97.9 61.3 | 91.7 66.1 | 94.8 64.7 | 95.5 82.5 | 92.0 53.3 | 81.7 48.1 | 90.6 47.0 | 93.5 

Table 5: Gender classification accuracy, in percent, by age range and gender (Female | Male).

Results and notable observations:

•	 All of the algorithms show a significant decrease in gender classification accuracy as age increases for adult females.

There is an empirically decreasing trend in accuracy seen in females past age 50, which implies algorithms are

misclassifying females as males more often as age increases. Accuracy for males across increasing age groups does

not demonstrate such evident decrease but remains comparatively stable. The correlation between age and gender

classification error observed in older females presents an opportunity where gender classification could be used

in conjunction with age estimation to mitigate the loss in accuracy in older women, for example, by moving the

minimum ”femaleness” threshold to a lower value if the person’s estimated age is above a certain threshold. Figure

4 shows examples of misclassifications in females above age 50.

•	 For females, gender classification is the most accurate in young adults (ages 21-30) as seen across all algorithms.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.3 ETHNICITY FRVT 9

Contrastingly, the highest accuracy is observed in older adult age groups (ages 31-60) for males. Gender classification

is more accurate in adult males (ages 21-60) than young boys (ages 0-10) for all of the algorithms.

Figure 4: Examples of misclassifications in females above age 50 extracted from the FERET database, which are representative of image

qualities from misclassifications observed in the operational dataset.

3.3 Ethnicity

While the effects of racial features on gender classification have been studied [9, 19] in the academic literature, the experimental results were derived using small datasets with a limited set of ethnicity groups. The visa dataset was used to

investigate the effect of ethnicity on gender classification as it contains a respectable number of visa images across multiple

ethnic proxies. The term ethnic proxy is used, because an individual could be a citizen of a country but not necessarily be

of that country’s ethnic descent. Ethnic proxy groups with a minimum of at least 1 800 images of subjects between ages

11-40 and a relatively balanced number of males and females were extracted and used in the analysis captured in Tables 6

and Figure 5.

ARG BRZL CHIN COL DF IND ISRL JPN KOR PERU PHIL POL RUS TWAN

B30D 95.0 97.7 96.4 96.4 99.0 97.2 97.8 90.0 94.4 97.2 97.9 98.1 97.6 94.1

B31D 95.3 97.8 96.7 96.7 99.0 97.7 98.0 90.1 94.3 97.6 98.1 98.2 97.7 94.0

C30D 93.3 95.0 89.2 94.6 96.7 98.1 96.4 82.9 83.6 95.4 92.8 97.3 96.8 81.5

E30D 91.2 96.6 96.4 95.0 97.2 97.8 96.6 95.1 95.6 96.8 97.7 97.5 96.9 96.1

E31D 89.8 97.0 94.4 94.7 96.1 96.9 95.8 96.7 94.9 96.9 97.6 97.7 96.6 96.8

E32D 93.9 97.2 96.6 95.5 97.6 98.4 97.3 95.8 95.8 97.5 98.0 98.1 97.6 97.1

F30D 80.9 86.4 80.8 86.2 83.6 82.5 87.1 84.3 81.7 86.2 78.6 89.8 87.8 76.1

K10D 86.8 91.2 89.6 87.0 88.1 94.6 94.5 85.5 86.0 90.1 90.9 92.6 91.4 88.4

P30D 90.8 95.0 93.2 93.8 96.1 96.0 95.3 89.2 90.5 95.0 95.3 94.5 94.8 88.9

Table 6: Overall gender classification accuracy, in percentage, by ethnic proxy group3. For each algorithm, the lowest accuracy number

is highlighted in yellow, and the highest accuracy number is highlighted in green.

3DF is Mexico City.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.3 ETHNICITY FRVT 10 Density 

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97.5 95.5 

99.2 98.8 

99.3 95.1 

97.6 98.0 

78.8 99.2 

93.3 95.5 

96.7 97.7 

98.1 97.6 

98.5 97.7 

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89.6 98.6 

93.7 97.0 

96.3 99.4 

97.3 95.8 

97.5 96.0 

99.2 98.8 

99.5 95.8 

97.5 98.5 

78.8 99.3 

92.2 96.5 

96.9 98.2 

98.4 97.7 

98.0 98.4 

96.7 98.7 

88.8 99.1 

89.1 97.6 

90.9 99.3 

82.0 99.3 

91.3 97.5 

94.1 99.3 

97.4 98.7 

94.2 98.7 

62.6 99.5 

67.8 99.2 

92.2 98.5 

88.9 99.3 

96.9 97.8 

95.3 98.3 

63.2 99.5 

93.8 88.5 

95.5 97.8 

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68.9 96.9 

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80.5 91.6 

68.5 94.9 

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54.3 97.7 

83.8 90.0 

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90.5 88.2 

90.8 83.8 

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95.3 93.8 

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97.5 94.6 

98.2 93.9 

94.0 96.6 

77.8 98.4 

90.1 90.9 

95.0 95.0 

93.5 98.2 

94.9 94.2 

91.9 97.5 

81.0 96.6 

ARG BRZL CHIN COL DF IND ISRL JPN KOR PERU PHIL POL RUS TWAN 

F M 

0.000.250.500.751.000.000.250.500.751.000.000.250.500.751.000.000.250.500.751.000.000.250.500.751.000.000.250.500.751.000.000.250.500.751.000.000.250.500.751.000.000.250.500.751.00 

Maleness−Femaleness Value 

Figure 5: Density plots showing distribution of maleness-femaleness value across various ethnic proxies3. Plots were generated for

ethnic proxy groups with at least 1 800 images of subjects between ages 11-40. Gender classification accuracy for male and female are

shown in the upper left and right of each graph, respectively.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.4 MULTIPLE IMAGE SAMPLES FRVT	 11

Results and notable observations:

•	 A majority of the algorithms show the lowest gender classification accuracy on subjects from Taiwan and Japan. It

can be observed from Figure 5 that males are more often misclassified as females on people from these east Asian

countries, which is a consistent trend between most of the algorithms. Algorithms F30D and K10D did not provide

continuous values for maleness-femaleness.

•	 The higher misclassification rates observed in males from the Taiwan and Japan may anecdotally be related to slimmer and more defined face shapes seen in Asian men, facial hair being an uncommon trait, and the popularity

of longer hairstyles, especially in Japanese men, but further research would be required to objectively verify these

conjectures.

3.4 Multiple Image Samples

In certain applications, there are opportunities for multi-sampling of images, such as imagery being captured from video

of people walking past a digital sign. For such scenarios, the question arises of whether accuracy improves if the gender

classification implementation is provided multiple contemporaneous images of the same subject. This could drive whether

a system, for example, used for targeted digital marketing, could set a minimum threshold on the number images of

a person to process, based on some time-accuracy tradeoff, prior to making a decision on the type of advertisement to

display.

The FRVT API [12] supports multiple still image input to the algorithm software for gender classification, which enables

the analysis of gender classification performance versus the number of image samples of the same person. The mugshot

dataset includes K > 1 contemporaneous images for some subjects, with contemporaneous, here, being defined as images

of the same subject collected within a twelve month span. This allows for the modeling of a scenario where gender

classification implementations can exploit multiple images. 11 920 subjects with at least four contemporaneous mugshot

images were extracted. Figure 6 shows the effects of the number of image samples on classification accuracy for the

algorithms that supported multiple image input. Classification Accuracy (%) 

99 

98 

97 

96 

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93 

B30D B31D C30D E30D E31D E32D F30D P30D 

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1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 

Number of Images 

Figure 6: Line plots showing gender classification accuracy vs. the number of image samples per subject. Plots were generated with

11 920 subjects.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng

4 
3.5 IN THE WILD FRVT	 12 Gender Classification Time (sec) 1.00 

0.75 

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B30D B31D C30D E30D E31D E32D F30D P30D 

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 

Number of Images 

Figure 7: Boxplots summarizing gender classification time vs. the number of image samples per subject. Plots were generated with

11 920 subjects.

Results and notable observations:

•	 Figure 6 shows most of the algorithms demonstrate an increasing trend in gender classification accuracy as the

number of image samples increases. Algorithms exhibit a decrease in misclassification rate of up to 50% between

one and four images, depending on the provider. Algorithm K10D did not support multiple image input.

•	 Figure 7 shows gender classification durations increasing linearly with respect to the number of image samples, as

expected.

3.5 In the Wild

In recent years, the study of gender classification on face databases acquired in the wild (i.e., unconstrained face images

that show a large range of variation in factors such as pose, illumination, facial expression, and background) has gained

popularity in the published literature. Among such unconstrained datasets are Images of Groups (GROUPS) [10] and

Labeled Faces in the Wild (LFW) [15]. GROUPS consists of images of groups of people collected from Flickr, and in all, the

dataset consists of 28 231 (14 559 females and 13 672 males) largely low resolution faces that are labeled with gender. The

LFW dataset consists of 13 233 images (2 977 females and 10 256 males) of famous people collected over the Internet. See

Section 2.3 for more details on these datasets.

Tables 7 and 8 tabulate the performance of FRVT participants on the GROUPS and LFW datasets, alongside results of

methods published from the academic literature.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.5 IN THE WILD FRVT	 13

Algorithm Overall

Accuracy (%)

B30D 86.3

B31D 86.2

C30D 68.7

E30D 90.3

E31D 90.2

E32D 90.4

F30D 71.1

K10D 71.3

P30D 83.5

Publication Overall

Accuracy (%)

# Images

Gallagher and Chen

(2009) [10]

74.1 25 099

Shan (2010) [22] 77.4 12 080

Dago-Casas et al.

(2011) [6]

86.6 14 760

Han and Jain

(2014) [14]

87.1 28 231

Bekios-Calfa et al.

(2014) [4]

80.5 Faces of children 

age 12 were

excluded.

(a) FRVT participants, using lights-out, black-box (b) Published methods,

testing protocol over entire dataset (28 231 images) using various testing protocols and images

Table 7: Tables summarizing overall gender classification accuracy on the GROUPS dataset.

Algorithm Overall

Accuracy (%)

B30D 94.4

B31D 94.4

C30D 84.5

E30D 95.0

E31D 95.2

E32D 94.9

F30D 79.1

K10D 76.2

P30D 93.9

Publication Overall

Accuracy (%)

# Images

Dago-Casas et al.

(2011) [6]

94.0 13 233

Shan (2012) [23] 94.8 7 443

Tapia and Perez

(2013) [24]

98.0 7 443

Bekios-Calfa et al.

(2014) [4]

79.5 13 233

Shaefey et al.

(2014) [7]

94.6 13 233

Ren and Li (2014) [21] 98.0 6 840

(a) FRVT participants, using lights-out, black-box (b) Published methods,

testing protocol over entire dataset (13 233 images) using various testing protocols and images

Table 8: Tables summarizing overall gender classification accuracy on the LFW dataset.

Results and notable observations:

•	 For both datasets, the top performing FRVT participants achieved comparable or higher overall gender classification

accuracy when compared to published methods that tested their algorithms with the same number of images.

•	 While standard evaluation protocols for gender classification have been proposed for these datasets [1], straight

comparison between performance results remains to be a challenge given some methods are tested on only a subset

of the images (e.g. excluding non-frontal images or children) and others using single versus cross-database validation, which in all cases, allowed the implementation to train on a set of images through every iteration of testing. For

the FRVT participant results documented, all images from the datasets were used for testing, and NIST employed

a lights-out, black-box testing methodology that did not involve any type of algorithm training during evaluation.

This is designed to model operational reality where software is shipped and used ”as-is” without subsequent algorithmic training.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.6 CONSTRAINED VERSUS IN THE WILD FRVT 14

3.6 Constrained versus In the Wild

The performance of gender classification over constrained and in the wild datasets are presented in Sections 3.1 and 3.5, respectively. Figure 8a contains sample photos extracted from the publicly available Multiple Encounter Dataset (MEDS) [2],

which are representative of image qualities from

the constrained dataset. Figure 8b contains sample

photos from the public in the wild datasets used in

this study, which contain images that exhibit a large

range of variation in pose, illumination, facial expression, and background. Comparative analysis of

the performance on the two different types of images would reveal whether gender classification is

affected by factors such as pose, illumination, facial

expression, and background. Figure 9 compares the

distribution of the maleness-femaleness value, and

Table 9 presents the average maleness-femaleness

value, by gender for constrained versus in the wild

images. The constrained dataset discussed in Section

3.1, and the aggregate of the two in the wild datasets

discussed in Section 3.5 were used in this study.

(a) Constrained (b) In the Wild

Figure 8: Examples of constrained images extracted from the MEDS

dataset and in the wild images extracted from LFW and GROUPS.

B30D B31D C30D E30D E31D E32D P30D 

0 

2 

4 

6 

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F M 

Constrained In the Wild 

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Maleness−Femaleness Value 

Figure 9: Density plots showing distribution of maleness-femaleness value, by gender and image type (constrained versus in the wild

images).

Algorithm Male Female

Constrained In the wild Constrained In the wild

B30D 0.14 0.27 0.77 0.76

B31D 0.14 0.28 0.76 0.75

C30D 0.18 0.37 0.71 0.60

E30D 0.07 0.16 0.83 0.84

E31D 0.07 0.15 0.84 0.82

E32D 0.07 0.17 0.88 0.85

P30D 0.31 0.38 0.63 0.66

Table 9: Average maleness-femaleness value, by gender and image type (constrained versus in the wild).

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
0.0002

0.0005

0.0010

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0.0100

0.0200

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0.1000

0.2000

0.5000

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0.00020.00050.00100.00200.00500.01000.02000.05000.10000.20000.50001.0000

False Female Rate (FFR)

False Male Rate (FMR)

B30D B31D C30D E30D E31D E32D P30D

3.7 SPECIFIC APPLICATIONS FRVT	 15

Results and notable observations:

•	 A majority of the algorithms show visible shifting of the distribution of maleness-femaleness value towards the

middle and, the increase in overlap between male and female values is indicative that the qualities of the in the wild

images are causing a decrease in algorithm certainty of gender classification. This observation is further supported

by the comparison of average maleness-femaleness value where for all of the algorithms, the average maleness

value is lower in constrained images (more certainty of maleness) and for a majority of the algorithms, the average

femaleness value is higher in constrained images (more certainty of femaleness).

•	 Based on the average maleness-femaleness value, the impact of the qualities of images collected in the wild appear

to have a bigger impact on males than females, given the larger differences observed in the average values for males.

3.7 Specific Applications

3.7.1 Gender Verification

Gender-targeted surveillance can assist with monitoring gender-restricted areas and potentially aid in filtering subjects

of interest - for example, in the event where elevated threat levels may be associated with a specific gender. Consider a

surveillance system with an automated gender clasB30D B31D C30D E30D E31D E32D P30D sification system that has the ability to filter and

highlight subjects based on gender. In a scenario

where areas restricted to females (e.g., entrance to

0.0002 

0.0005 

0.0010 

0.0020 

0.0050 

0.0100 

0.0200 

0.0500 

0.1000 

0.2000 

0.5000 

1.0000 

False Male Rate (FMR) 

1 

2 

3 

4 

5 

6 

7 

Algorithm 

B30D 

B31D 

C30D 

E30D 

E31D 

E32D 

P30D 

FMR @ FFR = .01 

0.13 

0.13 

0.25 

0.16 

0.16 

0.10 

0.31 

female restrooms or locker rooms) are being monitored, an operator may be alerted when a male is detected in view. In this case, one might set the cost of

falsely classifying a female as a male (i.e., the false

male rate) to the inconvenience of having to review

the alert. The cost of falsely classifying a male as a

female (i.e., the false female rate) could result in allowing suspicious or threatening activity to be conducted. Given it would be reasonable to argue that

the costs are asymmetric in this scenario, i.e., the

cost of a false female is greater than that of a false

male, tighter confidence levels could be set to min0.0002 0.0005 0.0010 0.0020 0.0050 0.0100 0.0200 0.0500 0.1000 0.2000 0.5000 1.0000 

imize false females. Figure 10 presents DET accuFalse Female Rate (FFR) 

Figure 10: DET curve plotting false male rate against false female rate for

gender classification. Plot was generated over 218,525 images of subjects

racy for gender classification for algorithms that supplied continuous maleness-femaleness values (F30D

and K10D did not provide continuous malenessbetween age 11-90. femaleness values). A false female rate of 0.01 would

impose a false male rate of 0.10 for the most accurate

algorithm (E32D) at that threshold. In other words, if a system were to ensure that 99% of monitored subjects entering a

female locker room were indeed female, it would mean that 10% of the time, the operator is falsely alerted of a male being

detected in view. The same Detection Error Tradeoff (DET) analysis can be done by gender and for different verification

thresholds to support specific applications. While the results documented are generated from single image input, analysis with multiple image input (i.e., sequential frames from video) may further solidify the results observed and be more

representative of surveillance systems running in video mode.

3.7.2 Sketches

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. That sketch

could then be used to try and match against photographs resident in a mugshot database. Given gender can be utilized

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
3.7 SPECIFIC APPLICATIONS FRVT 16

as an indexing technique to reduce the search space for automatic face recognition, the accuracy of gender classification

algorithms on sketches has an interesting niche application.

The City University of Hong Kong published the CUHK Face Sketch FERET Database (CUFSF), which is composed of

sketch images of subjects from the FERET dataset. A set of 856 sketches from CUFSF was used for this study. Figure 11

shows examples of the sketches, alongside the original FERET image, and Table 10 presents the gender classification accuracy of algorithms on sketches, alongside the performance on the corresponding photographs from the FERET database.

Figure 11: Images and sketches of subjects from the FERET database. This production of a sketch is atypical operationally given it is

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

Algorithm Accuracy on 

sketches (%) 

Failure to compute 

on sketches (%)

Accuracy on 

photos (%) 

B30D 90.9 0.0 97.5 

B31D 93.8 0.0 98.0 

C30D 85.6 0.0 96.3 

E30D 82.2 87.5 96.3 

E31D 85.0 87.5 96.1 

E32D 84.1 87.5 96.5 

F30D 68.8 0.0 85.5 

K10D N/A 100.0 87.3 

P30D 85.1 0.1 92.2 

Table 10: Table summarizing overall gender classification accuracy on sketches, failure to compute percentages on sketches, and accuracy on original FERET photos.

As gender classification on sketches was never declared to be a part of the study, the algorithms are being used in a manner

not expressly intended by the providers. Operationally though, automated face recognition algorithms are being used to

recognize sketches in many police departments [11], and gender classification on sketches may help reduce the search

space for face recognition in this scenario.

The use of this image set probably does reveal differences in algorithmic capability. Variation will in part depend on what

facial information is represented. Participant E and K’s algorithms exhibit very high failure to compute percentages on the

sketch images, with the reason being ”involuntary failure to extract features from the image” (as indicated in the FRVT

API). All of the algorithms perform better on the FERET photographs than their corresponding sketches, which is a likely

indication that there is information in the photographs used for gender classification that may not be represented in the

sketch images. While many of the algorithms give very high classification accuracy on the FERET photographs, the most

accurate algorithm on sketches (B31D) classifies gender correctly 93.8% of the time, which is better than some algorithms

even on the good quality photographs.

B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng
REFERENCES	 FRVT 17

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B = Cognitec C = Neurotechnology E = NEC F = Tsinghua University K = MITRE P = Zhuhai-Yisheng