IREX + MBE + 

MINEX I + MINEX II + PIV  

NIST update Session 

patrick.grother@nist.gov 
Chapter I :: MBE‐STILL 

Large‐Scale Face RecogniFon TesFng 
MBE OrganizaFon 

MBE – STILL-FACE TRACK 
MBE‐STILL :: What’s new about this test? 

• Largest public 1:N test ever 

conducted 

• N → 3 million 

• Enroll lifeFme history 

• Of visa images (DOS/DHS)  

• Of arrest images (FBI) 

• Vendor executes fusion 

• Provide operaFonal metadata to 

the algorithm 

• Sex  |  Height  |  Weight  |  Date of 

birth  |  Date of Picture 

• Report resource usage: 

• Time, storage, memory... 

• Pose conformance tesFng 

• QuanJfy deviaJon from standard. 

• Explicitly split 1:1 tesFng 

• With enrolled DB (Jme+aMend) 

• Without enrolled DB (e‐Passport) 

• Test iteraFvely 

• Provide feedback to developers 

• Drive R&D, improved performance 

• Support applicaFon of staFsFcal 

methods on the enrolled database 

• “Training” 

• Feature space separaJon, 

normalizaJon 
Chapter I :: IREX 

The Iris Exchange (IREX) Program 

SupporFng IRIS Interoperability 
IREX I :: Tested Image Formats 

Parent image from camera 

KIND 1 

Unsegmented polar 

Cropped image 

KIND 3 

Cropped and 

masked image 

KIND 7 

Reconstructed 

rectilinear 

KIND 48 

KIND 16 
Compression + Format RecommendaFons 

• Compression – Avoid it when you can! 

• Lossy compression does incremental damage to images. 

• Either no compression, or lossless may be sufficient. 

Lossless for 1:N 

Apps over networks 
RecogniFon Error Under Compression 

FALSE REJECT RATE 

Less compression: 

Lower FRR and FAR 

High compression: 

Higher FRR and FAR 

FALSE ACCEPT RATE 
In 144 pages IREX covers 

• Effect of lossy compression 

• JPEG vs. JPEG 2000 

• Limits of lossless compression 

• Effect of iris radius 

• How closely to crop the iris? 

• Comparison of specialized formats 

• Masked vs. Polar 

• Fit for purpose 

• Effect of pupil dilaFon  

• Change in dilaJon 

• Effect of eyelid occlusion 

• Effect of iris‐pupil displacement 

• Accuracy 

• ROCs 

• Fixed threshold – effect on FMR 

and FNMR 

• Speed accuracy tradeoffs 

• Template size 

• False Match Rate CalibraFon 

• How to set the threshold 

• Effect of dataset

• Algorithm interoperability 

• Enroll on A – IdenJfy on B 

• Image quality assessments 

• Biometric zoo 
IREX – Umbrella Program SupporFng Iris 

Interoperability 

L1 Pier 2.4 CrossMatch Seek 

Can an image 

acquired on the 

L1 PIER be 

matched 

against an 

image gathered 

on another 

sensor, for 

example the 

CrossMatch 

SEEK? 

Image based 

Interoperability 
Chapter II :: MINEX I 

Core Interoperability of MinuFa Templates 
MinuFa Matching 

Minutiae from enrollment image Minutiae from verification image 
Interoperability of MinuFa Templates 

False Non‐Match Rate  Supplier of VerificaFon Template + 

at False Match Rate  Template Matcher 

of 0.01  NEC 

NEC  0.0129 

Supplier of 

Enrollment 

Template 

Red values refer to NATIVE performance :  One vendor generates both templates 

and matches them. 
Interoperability of MinuFa Templates 

False Non‐Match Rate  Supplier of VerificaFon Template + 

at False Match Rate  Template Matcher 

of 0.01  NEC  Sagem 

NEC  0.0129  0.0205 

Supplier of 

Enrollment  Sagem  0.0316  0.0140 

Template 

Red values refer to NATIVE performance :  One vendor generates both templates 

and matches them. 
Interoperability of MinuFa Templates 

False Non‐Match Rate  Supplier of VerificaFon Template + 

at False Match Rate  Template Matcher 

of 0.01  NEC  Sagem  Cogent 

NEC  0.0129  0.0205  0.0300 

Supplier of 

Enrollment  Sagem  0.0316  0.0140  0.0207 

Template 

Cogent  0.0417  0.0225  0.0136 

Red values refer to NATIVE performance :  One vendor generates both templates 

and matches them. 
MINEX 

• FIPS 201 regulates PIV 

• Binding on agencies under FISMA 

• Supported by special publicaFons 

• SP 800‐76‐1  Biometrics 

• SP 800‐73  PIV Card Interfaces 

• MINEX is used for cerFficaFon against fixed 

performance interoperability specificaFon 

• Of 58 algorithms submiMed 

• 32 compliant INCITS 378 template generators 

• 26 compliant INCITS 378 template matchers 
Chapter III :: MINEX II 

On‐card comparison of ISO/IEC 19794‐2 

minuFa records on ISO/IEC 7816 smart 

cards 
MINEX II – Algorithms on Cards 

Verification Template Reference Template: sent via VERIFY sent via PUT DATA 

FMR 

FNMR 

DET 

Similarity Score 

via GET DATA 
MINEX II ObjecFves 

• Given ISO/IEC 7816 cards are unpowered crypto‐tokens 

with limited computaFonal resources 

• Can on‐card accuracy approach off‐card? 

• What’s the duraJon? 

• Test standardized as ISO/IEC 19795‐7 at final (FDIS) ballot. 

• As a by‐product 

• Define procedure for conversion INCITS 378 as a parent for 

ISO/IEC 19794‐2:2005 compact format: 

• Drop minuJa → Sort minuJa → Drop ResoluJon → QuanJze theta 

• QuanJfy accuracy loss 

• Open source code 
Public Report due 

June 2010 

MINEX II :: Now in fourth phase 

PHASE I 

□ Jul 07 ‐ Oct, 07 

□ 11 algorithms 

□ Precise‐TecSec 

□ Neurotechno‐IRM 

□ Sagem Orga 

□ Oberthur‐id3 

PHASE II 

□ Nov 07 ‐ Feb 08 

□ 9 algorithms 

□ G+D 

□ Sagem Orga 

□ Oberthur ‐id3 

Phase III 

□ Nov 08 ‐ May 09 

□ 7 algorithms 

□ Gemalto‐Micro‐

Packs 

□ Gemalto‐Cogent 

□ Gemalto‐Innovatrix 

□ Oberthur‐id3 

Phase IV 

□ Feb 19, 2010 

□ 17 algorithms 

□ 8 card suppliers 

□ 8 algorithm 

suppliers 

□ Non‐public  □ Public Report  □ Public Report 

May 2009  as 

NISTIR 7477 (Rev) 

NIST Interagency 

Report 7477 

□

ParFcipant = Team of { Card vendor + Fingerprint matcher 

vendor } 
Chapter II :: PIV 

An update on the Biometric Components 

of the Personal IdenFty VerificaFon 

SpecificaFon 
PIV :: Documentary History 

• FIPS 201 

• Legally binding on USG 

agencies 

• Points to NIST Special Pubs 

• NIST Special Pub 800‐76 

• Version ‐ 0:  January 2006 

• Version ‐ 1:  February 2007 

• Version ‐ 2:  Spring 2010 
Iris specificaFons for PIV 

• SP 800‐76‐2 specificaFons(under consideraFon):

Cropped • Iris image (for card)   masked 

image • Iris image (for CMS) 

• Camera specificaJons  KIND 7 

• Performance  Cropped 

• Capture API  image 

• Conformance tesJng  KIND 3 

• Comment is welcome 

• Now, and 

• on drao of SP 800‐76‐2 
IREX Test (Support for 1:1 and 1:N) 

• NIST Interagency Report 7629, Sep 21, 2009 

• Performance of Iris Recogni0on Algorithms on Standard Images 

• QuanFtaFve support for ISO 19794‐6 standard 

• Image size is about 3KB (for 1:1) and ~30KB (for 1:N) 

• Compression, cropping, formarng profiles 

• Speed‐accuracy tradespace 

• Ten implementaFons of standardized interoperable iris image 

format 

• Num. iris providers has expand x10 in last five years 

• Num core technology providers in iris exceeds that for face recogniJon

• Iris image interoperability superior minuFa interoperability 

• Less dependency on the product that prepares the record 

• hpp://iris.nist.gov/irex  (or google “iris interoperability”) 
Match‐on‐Card in PIV :: Near Term 

• Possible near‐term way forward: 

• Use a separate MOC applicaJon for card acJvaJon 

• Use a trimmed down version of the MINEX II MOC interface 

for the APDUs 

• It has been implemented by nine organizaJons, and used successfully 

• It’s openly documented in NIST Interagency Report 7485 

• Use of two fingers would saJsfy FIPS 140‐2 requirements on 

false match 

• Contact interface 

• GICS compliant 
Match‐on‐Card in PIV :: Longer Term 

• Longer term way forward 

• Formally include MOC in a future FIPS 201‐2 

• For card acJvaJon, and 

• As an authenJcaJon mechanism 

• Leverage NIST IR 7485 for interface 

• Leverage NIST IR 7452 for confidenJality 

• Or implementaJons of 7816 secure messaging 

• Meet FIPS 140‐3 (not FIPS 140‐2) 

• Consider ISO/IEC 24787 
Thank You 

patrick.grother@nist.gov 

301 975 4157 
The role of Interoperable Images 

The old paradigm 

The new paradigm 

IG 100 

AOPTIX 

Proprietary 

template (iris code) 

typically ≤ 1KB. 

Standardized 

Iris Image Record 

ISO/IEC 19794-6 

2KB to 308KB 

Supplier B 

Supplier C 

Single supplier 

iris recognition 

algorithm 

Supplier A of iris 

recognition 

algorithms 
DoD :: OperaFonal Context 

• QuesFon to Tom Dee during Keynote Q+A, DOD Biometrics + 

Forensics Summit, San Diego, May 12, 2009: 

• “Has any progress been made on gerng the biometric data from 

OCONUS to CONUS?” ... “This is like moving a bowling ball down a 

garden hose”. [Special Ops, Ft. Bragg] 

• QuesFon to breakout S&T Panel, May 12, 2009 PM: 

• “Is anyone working on compression, reducing the data for COMMs”. 

[Marine Corps Sys. Command] 
Why compress? 

• Small pipes, data is “big”, many transacFons 

• Net‐centric 

• DHS :: Ports of Entry → IDENT 

• DoD :: BaMlefield → ABIS 

• Card‐centric 

• CredenJals (USG, PIV, FRAC, CAC) :: Smart cards, 

• Example: 

• AEGIS Destroyer to CONUS.  With a channel at 128 

kbps, biometric = single 640x480 uncompressed iris 

~ 308KB → 19.25 seconds 
IREX I :: Highlights 

• 10 RecogniFon Algorithm  • Support interoperable images 

Companies  • Replace template‐based 

exchange  • 9 Commercial, 1 Academic 

• Standards conformant products  • Evaluate formats 

ready‐to‐go  • Establish limits of compression 

• Three iris datasets  • QuanFtaFve support for the 

• Approx 10000 persons, and  standards 

100000 images.  • ISO standard images 

• Largest public independent  • Binary record AND 

evaluaFon to date  • SemanJc properJes of images 

• ANSI/NIST  • Not a test of cameras or 

systems 

• Not a predicJon of operaJonal 

accuracy 
Relevance to HSPDs 

• Existence of standardized interoperable iris image 

records supports 

• Counter terrorism objecJves of HSPD 6, 11, 24 

• Cross agency / government exchange 

• QuanFfied compression response addresses 

• Real operaJonal bandwidth constraints (e.g. HSPD‐24) 

• Use of compressed iris data in blue‐force applicaJons (e.g. as 

a standardized data element on PIV/CAC cards for HSPD‐12) 
Iris data to go on PIV Cards 

Following the arrangement of fingerprint minutia data on 

current PIV cards... Two iris in one container. 

Tagged biometric 

container (SP 800-73) 

2.65M cards issued 07/2009 

CBEFF 

Header =88 bytes 

ISO Iris 

Image 

Header ≥107 bytes 

ISO Iris 

Image 

Data ~2 * 3KB 

CBEFF 

Signature 

block ~ 500 bytes 
Chapter III :: MBE‐STILL 

1:N Face tesFng tailored to the FBI Next 

GeneraFon IdenFficaFon Program 
Exploit all prior encounters 

Enroll all 

prior 

encounters

under a 

single ID 

Identification of a new 

Image 

The MBE-STILL API supports 

the idea of a MULTIFACE, each 

tagged with metadata. 

Vendor implementation 

determines fusion strategy. 

Transmit 

to NGI 

or ABIS etc 
MBE‐2D STILL :: Test Design ObjecFves I 

• Maintain virtues of technology / offline tesFng 

• Level playing field for comparaJve tesJng 

• Repeatability, traceability 

• Add maturity to the tesFng capability 

• Publish a re‐usable API;  Solicit public / supplier comments on API 

• Measure duraJon of all funcJon calls 

• Measure template size 

• Test iteraJvely :: Provide results + feedback to vendors 

• Making a technology test have operaFonal realism 

• Use operaJonal data! 

• Extend populaJon size to N > 106

• Execute a proper idenJficaJon test  

• Don’t model 1:N as being N 1:1s 

• Open‐universe (use impostors, use true impostors) search 

• Make sample data available in advance 
MBE‐2D STILL :: Test Design ObjecFves II 

• To support face recogniFon accuracy via data 

• Exploit mulJple historical images of an individual 

• Allow the implementaJon to execute fusion 

• Exploit operaJonal metadata :: Provide to implementaJon 

• Date of capture | Date of birth | Height | Weight | Sex 

• Allow implementaJon to execute post‐enrollment 

processing on the enrolled database 

• Feature space normalizaJon, for example. 

• To support face recogniFon accuracy via structuring the API 

to allow algorithms, Support Performance 
1:N in FBI (and USG) 

CHARACTERISTICS OF THE FBI TASK

• One‐to‐many, with N → 108 

• Aided by mulFple arrest records 

• i.e. mulJple enrolled images 

• A federated applicaFon 

• State + local + others → FBI 

• Heterogeneous images 

• Aided by face standards ... but 

• Pictures don’t conform to 

standards 

• Pictures vary despite mug‐shot 

standards 

• Aided by metadata 

• Age, locaJon, gender ... 

• Here now, and on the NGI radar 

TAILORED TESTING  TOWARD IT

• Run a test in proper 1:N mode 

• Est: 106 people, 107 images 

• OperaJonal images 

• Run a test with K ≥ 1 enrollment 

images per subject 

• Use even the profile views 

• ImplementaJon exploits samples 

• Inform the SDK as to the 

properFes of the images 

• Subject specific: Ethnicity, sex 

• Image specific: Age, date 

• Run fast 

• Public review of open API 

• “Thin”, automaJc, test reports 

• FIFO parJcipaJon 
Standards – And deviaFons from... 

ISO Standard Expression Gaze Too close Pose Angle 
 

 

Startek Engineering Incorporated 

Identix Incorporated 

Integrated Biometrics, Inc. 

Ultra-Scan Corporation 

Antheus Technology 

SAGEM Morpho Inc. 

Aware, Inc. 

Precise Biometrics 

Motorola 

XTec, Inc. 

US Biometrics Corporation 

Aware, Inc. 

Secure Design KK 

Veridt

Neurotechnologija 

NEC Corporation 

Cross Match Technologies 

MINEX I 

• IniFal test of INCITS 378 

performance (vs. image) 

and interoperability 

between products 

BIO-key International, Inc. 

SPEX Forensics 

SecuGen Corporation 

Startek Engineering Incorporated 

123ID, Inc. 

Sonda Technologies Ltd. 

Aware, Inc. 

SONATEQ 

Griaule Tecnologia Ltda 

Precise Biometrics 

Startek Engineering Incorporated 

NITGen Co., Ltd. 
 

01 02 03 04 05 06 07 08 09 10 11 

 FRVT 2002 FRVT 2006 MBE 

M I N E X Ongoing 

MINEX II 

CORE 

MINUTIA 

INTEROP 

7816 ONCARD 

MATCHING 
Agenda 

• Iris RecogniFon (The IREX Program)

• Standardized, tested, mulJ‐vendor implemented standard 

iris images → expanding marketplace. 

• Iris Exchange (IREX) :: Relevance to HSPDs 24, 12, 11, 6. 

• IBPC Conference (NIST March 1‐5, 2010)

• Performance: How to  define,  how to  get  it,  how to test  it, 

how to procure it, how it's not the whole story. 

• Face RecogniFon (MBE‐STILL)

• TesJng tailored to the open‐set 1:N idenJficaJon problem 

(e.g. criminal / KST idenJficaJon, fraud detecJon, 

watchlists).