On the utility of extended fingerprint features: A study on pores

Extended fingerprint features are routinely used by latent examiners in forensic applications. They are now being considered for inclusion in automatic fingerprint identification systems (AFIS), particularly with the adoption of 1000ppi resolution in the Next Generation Identification (NGI) system. Earlier studies on this topic suffered from two limitations: (i) experiments were based on live scan images that are generally of good quality and contain smaller intra-class variations compared to ink images and (ii) the baseline minutiae matcher used to measure the additive value of extended features was not a state-of-the-art matcher. In this paper, we study the utility of pores, one of the most prevalent extended fingerprint features, on rolled ink fingerprint images at both 500ppi and 1000ppi resolution in the NIST SD30 database. The results show that the fingerprint image quality significantly affects the automatic extraction and matching accuracy of pores. Furthermore, the contribution of pores to the overall fingerprint recognition accuracy is miniscule when a COTS matcher is used for 500ppi rolled ink fingerprint images. The fusion between pore matcher and COTS minutiae matcher is a bit more effective on 1000ppi good quality rolled ink fingerprint images. We believe that these results will be useful in the design of next generation AFIS.

[1]  Jonathan D. Stosz,et al.  Automated system for fingerprint authentication using pores and ridge structure , 1994, Optics & Photonics.

[2]  Julian Fiérrez,et al.  Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification , 2008, IEEE Transactions on Information Forensics and Security.

[3]  David R. Ashbaugh,et al.  Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology , 1999 .

[4]  A. R. Roddy,et al.  Fingerprint features-statistical analysis and system performance estimates , 1997 .

[5]  Anil K. Jain,et al.  Likelihood Ratio-Based Biometric Score Fusion , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  David Zhang,et al.  Direct Pore Matching for Fingerprint Recognition , 2009, ICB.

[7]  David Zhang,et al.  Adaptive fingerprint pore modeling and extraction , 2010, Pattern Recognit..

[8]  Elham Tabassi,et al.  Fingerprint Image Quality , 2009, Encyclopedia of Biometrics.

[9]  Ashim K. Datta Advances in Fingerprint Technology , 2001 .

[10]  Anil K. Jain,et al.  Pores and Ridges: Fingerprint Matching Using Level 3 Features , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[11]  Anil K. Jain,et al.  Latent Fingerprint Matching , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Krzysztof Kryszczuk,et al.  Study of the Distinctiveness of Level 2 and Level 3 Features in Fragmentary Fingerprint Comparison , 2004, ECCV Workshop BioAW.

[13]  Henry C. Lee,et al.  Advances in Fingerprint Technology, Second Edition , 2001 .

[14]  Arun Ross,et al.  Handbook of Multibiometrics , 2006, The Kluwer international series on biometrics.

[15]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition, Second Edition , 2009 .