The Future of NFIQ 

Elham Tabassi 

March 1, 2010 

1 
Agenda 

∸ Welcome 

– Elham Tabassi 

∸ NFIQ:: past, present, future 

– Elham Tabassi 

∸ Retraining NFIQ based on BioDEV data 

– Markus Nuppeney (BSI) 

∸ Break 

∸ Discussion 

– All 

∸ Wrap up 

– firm ideas on way forward 

THE 2   FUTURE OF NFIQ – MARCH 1, 2010 
NIST fingerprint image quality 

- NIST developed NFIQ in 2004 

∸ Open source 

- Key innovation: quality as a rank statistic for 

performance 

- NFIQ is a machine learning algorithm 

∸ Exploratory variables: image properties (minutiae, 

ridge density and clarity) 

∸ Response variable: separation of genuine and 

impostor comparison 

THE 3   FUTURE OF NFIQ – MARCH 1, 2010 
quality 

number 

NFIQ 

- feature extraction: computes appropriate signal or image fidelity 

characteristics and results in an 11-dimensional feature vector. 

- neural network:classifies feature vectors into five classes of 

quality based on various quantiles of the normalized match score 

distribution. 

- quality number: an integer value between 1(highest) and 5 

(poorest).

 feature       neural  

extrac>on      network

THE 4   FUTURE OF NFIQ – MARCH 1, 2010 
gray scale

fingerprint

image 

 

1 total # of minu5a 

 2 #of min. with q>.5   

 3 #of min. with q>.6 

 4 #of min. with q>.7 

 5 #of min. with q>.8 

 6 #of min. with q>.9

assessment of 

 clarity of ridges   

and valleys 

7 size of foreground      

8 quality zone 1 

9 quality zone 2      

10 quality zone 3     

11  quality zone 4 

NFIQ – feature vector 

NIST Minutiae detector (mindtct of NBIS distribution) 

has been used for feature extraction. 
1

f

NFIQ – training 

1dimen5onal

eature vector 

  

quality number 

{1,2,3,4,5} 

1 is the best and  

5 is the poorest 

training: 3900 

images of flat 

index fingers and 

thumbs 

A full similarity  neural network 

matrix of the 

training set is 

needed to 

compute the 

output class of 

neural network. 

THE FUTURE OF NFIQ – MARCH 1, 2010 
Quality: rank statistic for performance 

NFIQ=3

NFIQ=1

NFIQ=2

NFIQ=4

NFIQ=5

ALL

NFIQ::(fmr,fnmr) at fixed threshold 

THE 7   FUTURE OF NFIQ – MARCH 1, 2010 
Dependence on matcher FNMR 

NFIQ 

Each point corresponds to one algorithm. 

THE 8   FUTURE OF NFIQ – MARCH 1, 2010 
false non-match rate 

5 4 3 2 1 

NFIQ 

Statistically different level of performance 

How many levels?

THE 9   FUTURE OF NFIQ – MARCH 1, 2010 
 NFIQ rolled impressions -1 

THE 10   FUTURE OF NFIQ – MARCH 1, 2010 
 NFIQ rolled impressions -2 

THE 11   FUTURE OF NFIQ – MARCH 1, 2010 
NFIQ – test of time 

+ 

∸ Novel definition of 

biometric quality 

– performance related 

– accepted by the 

community 

∸ Interoperability 

– uniform interpretation 

– tuned to a class of 

matcher 

∸ Open source 

∸ Extensively examined 

– by NIST and others 

– tools for quality 

summarization, slap, ... 

_ 

∸ Aging 

– recognition technology has 

advanced since 2004. 

– feature vector could be 

improved 

∸ Efficiency 

– ~300 msec per image - not 

fast enough for real time 

– takes 4 times for 4-finger 

slap 

∸ Not enough levels 

– Still statistically significant 

∸ Not enough NFIQ 4 

THE FUTURE OF NFIQ – MARCH 1, 2010 
Options for NFIQ 2.0 

∸ Do nothing! 

∸ Incremental updates to NFIQ towards NFIQ 2.0 

– An improved NFIQ, generalized vanilla flavor 

• Improve feature extraction, training data, machine learning 

algorithm 

• done by NIST – open source 

– No ability to customize it to a particular application 

– Limited collaboration with industry 

∸ Modular NFIQ 2.0 

– Plug-and-play feature vector 

• Improves efficiency in field operations 

– NIST does the training using its large sets of data 

• Generalized or specialized to a particular comparison algorithm 

• Feedback to vendors 

– Expands the marketplace of interoperable products 

• Calibrated quality in standardized range [0,100] 

We like to hear your comments, sugges5ons.  13 

THE FUTURE OF NFIQ – MARCH 1, 2010 
Modular NFIQ 2.0 

14 

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, sugges5ons.

THE FUTURE OF NFIQ – MARCH 1, 2010 
http://www.itl.nist.gov/iad/894.03/quality/

http://www.itl.nist.gov/iad/894.03/nigos/ 

DISCUSSION 

THE 15   FUTURE OF NFIQ – MARCH 1, 2010 
Q1. dedicated or generalized quality? 

⚀ generalized, matchers/extractors may 

change. 

⚁ dedicated – the deployed matcher / 

extractor is all that is important. 

⚂ both – each had its own utility. 

⚃ I don’t care. 

THE FUTURE OF NFIQ – MARCH 1, 2010  16 
Q2. NFIQ 2.0 or not? 

⚀ Not – never liked NFIQ anyhow! 

⚁ NFIQ 1.0 was OK – but no need for NFIQ 

2.0. 

⚂ Yes – it is timely. 

⚃ Abstain. 

THE FUTURE OF NFIQ – MARCH 1, 2010  17 
Q3. modular NFIQ 2.0? 

⚀ makes sense 

⚁ bad idea 

⚂ complicated 

⚃ don’t care 

THE FUTURE OF NFIQ – MARCH 1, 2010  18 
Q4. the most reasonable option is ... 

⚀ option 1 - do nothing 

⚁ option 2 – vanilla flavor, generalized, 

open source NFIQ 2.0 

⚂ option 3 – modular NFIQ 

⚃ none of the above 

⚄ don’t care 

THE FUTURE OF NFIQ – MARCH 1, 2010  19 
Calibra>on Curve: Error vs reject : NFIQ 

February 25, 2010  elham.tabassi@nist.gov  20 
 NFIQ rolled impressions - 3 

THE 21   FUTURE OF NFIQ – MARCH 1, 2010