When Fingerprints Are Combined with Iris - A Case Study: FVC2004 and CASIA

This paper presents novel studies on fusion strategies for personal identification using fingerprint and iris biometrics. The purpose of our paper is to investigate whether the integration of iris and fingerprint biometrics can achieve performance that may not be possible using a single biometric technology. Moreover we are interested in evaluating the correlation among the best state of art algorithms for fingerprint verification presented at FVC2004. We show that the fusion among some competitors of FVC2004 permits a drastically reduction of the performance. Particularly interesting is the result obtained by combining the competitors of FVC2004 and an IRIS matcher in terms of EER (the most used parameter in the evaluation of real identification systems), significantly lower than for other approaches. This indicates that the intrinsic error of the system is very low and tends to 0 for some of the tests carried out. The results of this paper confirm that a multimodal biometric can overcome some of the limitations of a single biometric resulting in a substantial performance improvement.

[1]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[2]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[3]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[4]  Richard P. Wildes,et al.  A machine-vision system for iris recognition , 2005, Machine Vision and Applications.

[5]  Julian Fiérrez,et al.  Exploiting general knowledge in user-dependent fusion strategies for multimodal biometric verification , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[8]  Raffaele Cappelli,et al.  SFinGe : an Approach to Synthetic Fingerprint Generation , 2004 .

[9]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Arun Ross,et al.  Information fusion in biometrics , 2003, Pattern Recognit. Lett..

[11]  Stefan Fischer,et al.  Expert Conciliation for Multi Modal Person Authentication Systems by Bayesian Statistics , 1997, AVBPA.

[12]  Anil K. Jain,et al.  Performance evaluation of fingerprint verification systems , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[14]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  R. Sanchez-Reillo,et al.  Minimal template size for iris-recognition , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[16]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[17]  T. Tan,et al.  Iris Recognition Based on Multichannel Gabor Filtering , 2002 .

[18]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[19]  Anil K. Jain,et al.  FVC2000: Fingerprint Verification Competition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..