IREX III

Performance of 1:N Iris Recognition Algorit

Patrick Grother

Information Technology Laboratory

National Institute of Standards and Technology (US),

United States Department of Commerce

NIST Session, BCC, Tampa, Florida

Wednesday, September 28, 2011
IREX III

Sponsors IREX Team

» Patrick Grother» George Quinn

» James Matey» Elham Tabassi (IREX 

IREX Timeline

» API + CONOPS publis

Imagery » Algorithm submissio

February 2011 → Au

» Final report October 
IREX III :: By The Numbers

When, Where, Who» Two algorithm submission phases

• 1. February – June, 2011• 2. August 2011

» NIST

• Sequestered data• Up to 55 blades; Up to 880 cores,

each 192GB memory

» 11 organizations

• 2 academia, 9 commercial, 0 from

NIST

» Up to 10 algorithms per

organization

• 92 tested

How, how big» Parent Corpus

• 2212342 people• 4333745 eyes• 6142289 images

» Enrolment population

• 20K, 160K, 1.6M, 3.9

• 20K, 160K, 1.6M, tw

» Searches

• 239K mate• 314K nonmate

» Comparisons

• 239K mate searches• 1228 billion nonmat

• Using N = 3.9M en
1:N Search

Iris Search

Image Template

Template

N tem

En

Alice 0.02Bob 0.34Christophe 0.38David 0.39Ernie 0.45

Candidate List

FNIR, aka “Miss Rate”

FPIR

Aka False Alarm Rate
Open Set 1:N Search

Lights outIdentification

Operator Adjudication of

Search Results

As retrieved Saturday September 24, 2011, Jim

Henson’s 75th birthday
IREX III :: Measurements

Accuracy Resources» Accuracy

• FNIR ‐‐ Miss Rate• FPIR ‐‐ False Alarm Rate

» Template generation failure» Two eyes much better than one?

» Image quality values related to failure?» Interoperability :

• Enroll camera A, identify camera B

» Is iris ageing evident / important?

» Effect of geometry

• Does dilation make a difference?

• Iris diameter?

» Cumulative match

• Workload on (forensic) examiner

» 1:N Face vs. 1:N Iris

» Template size

• Enrolment template• Search template

» Time needed for

• Template generation• Search as function o

» Threaded operation vs

parallelism

» Memory usage

• Static vs. N• Dynamic vs. N
   

DET of Second Round Algorithm

False Positive Identification Rate, N = 3,900,000

False Negative Identification Rate 0.02 0.05 0.10

0.20
   

Scalability :: Accuracy dependence 

160K 1.6M 4M

False Positive Identification Rate (Algorithm T11B

160K 1.6M

0.020.050.040.03

False Negative Identification Rate
   

But ... control of FPIR

4M

1.6M

160K

False Positive Identification Rate (Algorithm V11B

160K

0.020.050.040.030.01

False Negative Identification Rate
   

Tradespaces :: Intra + Inter Suppl

0.2 seconds Median Search Time 2

False Negative Identification Rate

0.020.050.100.200.50
   

Tradespaces :: Intra + Inter Suppl

0.02 secs Median Template Generation Time 1 se

False Negative Identification Rate

0.020.050.100.200.50
IREX III – Two Documents under Prepar

Evaluation Report :: Oct 2011 Improving Iris Recog» NIST Interagency Report 8XXX » NIST Interagency Repo

» Vignettes from empiri

observations

» Guidance to planners, 

operators, deployers f

quality control

• Potential content fo

standard amendmen

» Guidance to develope

consideration in algor
Conclusions

» The industry has at least

eleven algorithm providers

» Algorithms matter

• Large variations in accuracy

» Iris can be fast, 107 mps

• And slow, 104 mps

• Speed can be traded for

accuracy

• Accuracy statements without

speed measurements arelimited

» The Daugman iriscode

template is not the only one

• Template sizes vary ~250B to

10KB, and up to 20K for

search.

» FPIR = N FMR usua

• But for others, FPI• Threshold calibrat

» Failure modes are 

dependent

• Valuable to review • Valuable to review 

nonmates
IBPC 2012

March 5‐9, Gaithersb» How to define, get, de

measure, assure, perf

biometric systems.

» http://biometrics.nist» 2012 website coming 

» Contact ibpc2012@n» See slides “The Gulf Be

Biometric Research anDeployments”, Terry B
Thank You

patrick.grother@nist.gov
   

Published UID Accuracy, N = 20

0.0001 0.001 0.01 0.1 1

False Positive Identification Rate, N = 20,000

False Negative Identification Rate 0.002 0.003 0.004 0.005 0.001 IREX 

1.6MUID 

Undassuand 

thaapacom

Source: Annexure 3, UID Enrolment Proof‐of‐Concept Report, December 3
Dilation, Search

Dilation, 

Dilation :: Outdoor operation implic

Dilation, 

Dilation, Search
Dilation = Rp / Ri

» False rejection via

• High dilation

• Cross condition

• Low dilation (i.e. constriction)

Dilation, Enrollment

Dilation, Search
False Positives

» Vanilla binomial

» FPIR(τ, N) = 1 – (1 – FMR(τ))N

» Which, for small FMR(τ), gives» FPIR(τ, N) ~ N FMR(τ)

False negatives

» For one enrolled m

» FNIR(τ, N) = FNMR

» To add a rank require

FNIR definition, e.g. “H

and rank 5 or better”, 

» Guide to Biometrics, B

Springer 2003, or

» Grother + Phillips, Mo

population recognitionperformance, CVPR 20