Deficiencies in NIST Fingerprint Image Quality Algorithm Predicting biometric performance using image quality metrics

Transcription

1 Deficiencies in NIST Fingerprint Image Quality Algorithm Predicting biometric performance using image quality metrics Martin Aastrup Olsen, Hochschule Darmstadt, CASED 12. Deutscher IT-Sicherheitskongress, Stadthalle Bonn-Bad Godesberg

2 Agenda Fingerprint Sample Quality Application Scenarios Quality Metrics NFIQ Overview NFIQ Areas of Improvements Plans on the Future Developments Dept. of Secure Services CASED Martin Aastrup Olsen 2

3 Fingerprint Sample Quality Ground truth quality Ground truth by visual inspection is complicated and expensive Prediction of matcher performance Reflects actual biometric system performance Global quality features Gives information on the overall sample quality Local quality feature Allows for the creation of quality maps Can identify problematic areas in a fingerprint Dept. of Secure Services CASED Martin Aastrup Olsen 3

4 Quality and Utility [ISO :2009(E)] Dept. of Secure Services CASED Martin Aastrup Olsen 4

5 Application Scenarios Enrollment quality assessment Quality assurance for existing databases Verification and identification quality assessment Negative authentication in bordercontrols Identity documents (npa, passports) Differential processing Thresholding Algorithm choice Dept. of Secure Services CASED Martin Aastrup Olsen 5

6 Related Work NFIQ by NIST (NISTIR 7151 [2004]) SIVV (NISTIR 7599 [2009]) NFIQ+ by SecuNet ISO Biometric sample quality Framework (ISO [2009]) ISO Biometric sample quality Fingerprint image data (ISO [2010]) Dept. of Secure Services CASED Martin Aastrup Olsen 6

7 Quality Metrics Should examine both global and local structures Orientation certainty Ridge-valley uniformity Orientation flow Frequency analysis Power spectrum Noise [Fernando-Alonso (2007): A Comparative Study of Fingerprint Image-Quality Estimation Methods] Dept. of Secure Services CASED Martin Aastrup Olsen 7

8 Orientation Flow [Fernando-Alonso (2007): A Comparative Study of Fingerprint Image-Quality Estimation Methods] Dept. of Secure Services CASED Martin Aastrup Olsen 8

9 Local Contrast [Fernando-Alonso (2007): A Comparative Study of Fingerprint Image-Quality Estimation Methods] Dept. of Secure Services CASED Martin Aastrup Olsen 9

10 Power Spectrum [Fernando-Alonso (2007): A Comparative Study of Fingerprint Image-Quality Estimation Methods] Dept. of Secure Services CASED Martin Aastrup Olsen 10

11 NFIQ Features Compute blockwise image quality Direction, contrast, low flow, high curve Combined to a quality map, 5 discrete levels MINDTCT detects and computes minutiae quality Minutia location in quality map Minutia unreliable if score < 0.5 [Fernando-Alonso (2007): A Comparative Study of Fingerprint Image-Quality Estimation Methods] Dept. of Secure Services CASED Martin Aastrup Olsen 11

12 NFIQ Overview 11 dimensional feature vector based on Minutia count Minutia quality > {0.5, 0.6, 0.75, 0.8, 0.9} Foreground blocks (Quality > 0) Quality map using local features: Direction field, contrast, low flow, high curvature Classifier based on neural network Dept. of Secure Services CASED Martin Aastrup Olsen 12

13 How NFIQ Determines Quality Training the classifier Compute similarity scores for all samples in dataset Compute target utility for each sample based on distance between genuine score and imposter distribution Bin fingerprints into 5 classes Train neural network with target utility and sample features Dept. of Secure Services CASED Martin Aastrup Olsen 13

14 Areas of Improvement for NFIQ Very coarse with only 5 discrete levels Higher fidelity required Trained with dataset with less than 5% samples of the two lowest qualities Biometric algorithms have developed since 2004 Image feature extraction Machine learning technology Dependency on minutiae data is not desirable Dept. of Secure Services CASED Martin Aastrup Olsen 14

15 NFIQ+ Initiated by SecuNet Retrain NN with other dataset 1098 individuals 9 imprints/finger, live-scan Indicates significance of quality related to performance Results show little predictive improvement but improvements possible through New image features and classifier Dept. of Secure Services CASED Martin Aastrup Olsen 15

16 NFIQ2.0 Goals Fingerprint quality assessment algorithm NFIQ2 Better predictive accuracy than NFIQ1.0 A lightweight implementation for mobile devices To give fast response in the field Calibration curves for specific fingerprint SDKs Become part of next NIST and ISO standard Dept. of Secure Services CASED Martin Aastrup Olsen 16

17 NFIQ2.0 Development plan The next 10 months Aquisition of vendor SDKs (Call for Participation ends in 1 week) Architecture and framework design New dataset composition We can utilize large data sets courtesy of NIST and BKA Improved utility definitions Strategies for binning to ensure high fidelity in the low quality range Identify new image quality features Preliminary results to be presented in March 2012 Dept. of Secure Services CASED Martin Aastrup Olsen 17

18 NFIQ2.0 Development plan contd. Future Testing, training, improving Defining the final feature vector Selection and training of classifier Calibration curves for single vendor Selection of features for lightweight implementation Focus on low resource use Dept. of Secure Services CASED Martin Aastrup Olsen 18

19 Collaborative Partners BKA BSI NIST SecuNet IGD Fraunhofer Hochschule Darmstadt Dept. of Secure Services CASED Martin Aastrup Olsen 19

20 Preliminary Results, ISO Metrics New image metrics are not trivial to identify Orientation Flow, c= = Power Spectrum, c = 0.18 Dept. of Secure Services CASED Martin Aastrup Olsen 20

21 Preliminary Results, ISO Binning Binning according to suggestions of ISO does not guarantee non-overlapping bins Dept. of Secure Services CASED Martin Aastrup Olsen 21

22 Final Remarks Our preliminary results indicate low correlation between quality metrics of ISO and utility scores The official Call for Participation is available at Still possible to submit SDKs Input from vendors, biometric experts, and organizations applying NFIQ is warmly welcome Contact information Martin Aastrup Olsen CASED Mornewegstr. 32 D Darmstadt Dept. of Secure Services CASED Martin Aastrup Olsen 22