secunet Security Networks A

Experiences with the retraini

NFIQ 

Gaithersburg, March 1st, 2010 Markus Nuppeney, Bundesamt für Sicherheit in der InformDr. Johannes Merkle, secunet Security Networks AG Marco Breitenstein, secunet Security Networks AG 
Agenda 

2 

Why retrain NFIQ? 

Training approach and results 

1 

2 

2 

Wrap-up / discussion of next steps 
Why retrain the NFIQ? 

Widely used reference Potential i

3 

▀ Recognized as a reference in 

fingerprint quality estimation Used for ePassport application e.g. in 

Germany Used for Visa application e.g. in 

Germany Often coded as quality estimate in 

fingerprint reference data 

Seen as a reference compared to 

alternative approaches 

▀▀▀▀

▀ Strange behavio

- E. g. quality value

Increase speed More granular qu

- Definition of more - More homogenou

Better suited traiapplication 

- E.g. no rolled or p

fingerprints for eD

▀

▀

▀
General Approach 

Match 

Scores 

Image 

Features 

Norm. Match

Score 

Estimated 

Quality Class 

Statistical Evaluation Estimation by Neural 

4 

Quality Class 
Thoughts on influences and parameters for 

fingerprint data basis 

- rolled / plai

sensors no. of imprints per finger 

n 

--

calculate comparison scores 

define NFIQ classes 

- fusing matching algorithms 

number & size of classes 

-

image feature extraction 

- selection 

of features no. of features extraction algorithm 

--

- matching algorithms - size of the 

similarity matrix 

- error handling - definition of 

norm. match scores 

Main changes in BSI/secunet retraining 

5

Different database Up-to-date Increase no. of 

(live scan, plain) matcher classes (10) 
Data basis 

▀ Original NFIQ used several databases 

- Both live-scans and inked imprints • Inked imprints not relevant for bord

- two imprints per finger • only one genuine score per finger 

- 50% as training set, 50% as test set 

▀ We used 9 live-scans of 8784 fingers, captured with 3 different se

- 8 genuine scores per finger 

•allows careful consideration of genuine score deviation by robust meas

- Computation of 450 match scores per imprint 

▀ We used 5 matching algorithms 

- NIST, Neurotechnology, L1/Identix, Dermalog, NEC - NEC SDK returns match score „0“ in case of non-match (internal threshold) 

5 4 

• match score statistics less significant 

3 

▀ 5 classes, resembling original NFIQ 

2 

6 

Distribution of NFIQ values 
7

Result ▀ Impact of neural network training parameters were small ▀ Evaluation on test-set shows slight improvement 

FMR 

FNMR 

Optimal classifier for 

given class definition 
Promising optimization potential 

-

fingerprint data basis 

- adapt variety of 

sensors 

define NFIQ classes 

calculate comparison scores 

image feature extraction 

- better 

or different features 

exact scores - more 

(NEC) classes 

- more matching 

algorithms 

- definition of 

normalized comparison score 

▀ DET curves show: 10 classes bear more potential than 5 ▀ Optimize class definition first ▀ Feature vector definition could have great optimization potential 

- E.g. apply neural network for quality assessment of minutiae 

8 
Leasons learned 

▀ Retraining is possible .. ▀ But: No ready to use toolbox ▀ Documentation should be extended for NFIQ 2.0 

- Detailed documentation of the training process is missing - Information needs to be gathered by source code examination - Results of the original NFIQ training process are not available in detail 

- would be useful for comparing results 

▀

9 

Big improvement of NFIQ performance seems to be possible 

- More examination necessary 
Wrap-up 

• NFIQ is highly needed 

• Clear potential for optimization 

• NIST's ideas for NFIQ 2.0 highly appreciated 

•

10 

Vendor-independent but modular NFIQ upg

Looking forward to an interesting discu
Thank you for your attention

secunet Security Networks AG Marco Breitenstein Head of Biometrics & eIDs +49 201 5454-3019 marco.breitenstein@secunet.com