Elham Tabassi 

March41, 2010 

1 
- NIST developed NFIQ in 2004 

∸ Open source 

- Key innova<on: quality as a rank sta<s<c for performance 

- NFIQ is a machine learning algorithm  

∸ Exploratory variables: image proper<es (minu<ae, ridge 

density and clarity) 

∸ Response variable: separa<on of genuine and impostor 

comparison 

∸ Can only do what it is trained for!  

∸ Thank you for using NFIQ. 

∸ And for feedbacks / lessons learned / .. 

IBPC March 4, 2010  tabassi@nist.gov  2 
- Macrh 1, 2010 

- Discussed way forward  

∸ Three op<ons 

- Need for calibra<on of quality scores 

IBPC March 4, 2010  tabassi@nist.gov  3 
- Do nothing! 

- Incremental updates to NFIQ towards NFIQ 2.0 

∸ An improved NFIQ, generalized vanilla flavor 

∹ Improve feature extrac<on, training data, machine learning 

algorithm, beZer matcher, more levels 

∹ done by NIST – open source 

∸ No ability to customize it to a par<cular applica<on 

∸ Limited collabora<on with industry 

- Modular NFIQ 2.0 

∸ Plug‐and‐play feature vector 

∹ Improves efficiency in field opera<ons 

∸ NIST does the training using its large sets of data 

∹ Generalized or specialized to a par<cular comparison algorithm  

∹ Feedback to vendors  

∸ Expands the marketplace of interoperable products 

∹ Calibrated quality in standardized range [0,100] 

4 

We like to hear your comments, sugges<ons. 

IBPC March 4, 2010  tabassi@nist.gov 
5 

quality  

Number 

[0,100] 

 feature       func*on  

extrac*on          approximator 

 by NIST 

(Clear box) 

vendor supplied 

(black box) 

Standardized 

feature + vendor  

proprietary 

(gray box) 

By NIST 

Generalized (or 

maybe algorithm 

dependent) 

We like to hear your comments, sugges<ons. 

IBPC :: NFIQ – MARCH 4, 2010 
Elham Tabassi 

NIST 

March 4, 2010 
- the 2nd ac<vity under IREX  

- funded by DHS S+T 

∸ Project “Radical improvement in iris quality 

assessment and maturing mul8modal biometric 

u8liza8on” 

- an evalua<on based program for 

development of clear, implementable, and  

interoperable iris quality standard ISO/IEC 

29794‐6. 

IBPC March 4, 2010  tabassi@nist.gov  7 
- Enabling scien<fic progress in iris image quality assessment 

∸ Iden<fying specific iris image proper<es that are influen<al on 

recogni<on accuracy, and quan<fying their effects 

∸ Evalua<on of iris image quality assessment algorithms 

∹ Effec<veness + Efficiency  

∹ Scalar and vector quality 

- Expand marketplace of interoperable products 

∸ Calibra<on of iris image quality assessment algorithms 

-  Support development of iris image quality standard (ISO/IEC 

29794‐6)  

∸ Strengthening the science behind the claims 

∸ Preven<ng over‐prescrip<ve statements  

∸ Introducing tolerance bounds on iris image covariates 

IBPC March 4, 2010  tabassi@nist.gov  8 
- Develop test scope, protocol, API 

∸ In consulta<on with industry and SC 37 WG 3 

- Invite par<cipa<on 

∸ Either as a provider of image quality assessment,  

∸ Or as a provider of generators AND matchers, 

∸ Or both  

- Technology being supplied to NIST as SDK 

∸ With IQCE API 

∸ Rounds of tes<ng 

- Offline archived datasets at NIST 

∸ Uncompressed raw images  

∸ Dedicated data collec<on with specific image impairments 

∹ Clarkson Q‐FIRE  

- Devise metrics + analysis of results  

tabassi@nist.gov  9 

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IBPC March 4, 2010 
October 2010   Final report 

May 2010  Submission period ends 

July 2010 SC 37 Working Group 3 mee<ng in Malaysia

February 1, 2010 

January 27, 2010

January 18‐22, 2010

Release of Clarkson Q‐FIRE sample data

Submission period opened. 

rounds of submit ‐> evaluate ‐> report

SC 37 Working Group 3 mee<ng in Singapore

January 12, 2010 Release of final evalua<on plan

November 27, 2009 Release of the 3rd  draq evalua<on plan, for comment. 

October 22, 2009 Release of the 2nd draq evalua<on plan, for comment. 

October 18, 2009 Comment on the 1st draq due

October 4, 2009 Launching IQCE. Release of 1st draq of test plan + API.

IBPC March 4, 2010  tabassi@nist.gov  10 
Cropped Image 

raw image  

VENDOR SUPPLIED IMAGE

QUALITY ASSESSMENT

ALGORITHM (IQAA) 

QUALITY VECTOR

1  Scalar quality 

2 .. 17  Defined (standard) quality metrics

18..32  Reserved  

33..64  Vendor‐defined quality metrics 

tabassi@nist.gov  11 

 Q 

FNMR 

IBPC March 4, 2010 
BLACK ICE 

iFMR=0.024 

iFNMR= 0.26 

CLEAR ICE 

iFMR=0.0001, 

iFNMR= 0.0 

BLUE OAT

iFMR =1.8e-4, 

iFNMR= 0.75 

 G BLUE WOLF 

iFMR=0.005 

 iFNMR=0.0 

IBPC March 4, 2010  tabassi@nist.gov  12 
Quality A1  Quality D1  Quality G1 

1. Highest quality with CLEAR ICE progressing down to 

lowest quality with BLACK ICE (IREX I report) 

2. Raw quality scores are not 

directly interoperable and 

some calibra<on is needed. 

IBPC March 4, 2010  tabassi@nist.gov  13 
- A refined list of iris image quality metrics with 

tolerance bounds 

- Quan<ta<ve support to ISO/IEC 29794‐6 

- Iris image quality tool box 

- Technical papers on iris image quality 

- mathema<cal equa<ons on how to compute quality e.g. SNR 

- Soqware implementa<ons 

- open source or proprietary compiled libraries 

- Calibra<on curve per IQAA. 

NSC visit  2/22/10  tabassi@nist.gov  14 
Elham Tabassi 

tabassi@nist.gov

301 975 5292 

IBPC March 4, 2010  tabassi@nist.gov  15