Quality-based Score Level Fusion in Multibiometric Systems

The quality of biometric samples has a significant impact on the accuracy of a matcher. Poor quality biometric samples often lead to incorrect matching results because the features extracted from these samples are not reliable. Therefore, dynamically assigning weights to the outputs of individual matchers based on the quality of the samples presented at the input of the matchers can improve the overall recognition performance of a multibiometric system. We propose a likelihood ratio-based fusion scheme that takes into account the quality of the biometric samples while combining the match scores provided by the matchers. Instead of estimating the quality of the template and query images individually, we estimate a single quality metric for each template-query pair based on the local image quality measures. Experiments on a database of 320 users with iris and fingerprint modalities demonstrate the advantages of utilizing the quality information in multibiometric systems

[1]  Samy Bengio,et al.  Improving Fusion with Margin-Derived Confidence in Biometric Authentication Tasks , 2005, AVBPA.

[2]  E. Lehmann.  Testing Statistical Hypotheses , 1960 .

[3]  Craig I. Watson,et al.  Fingerprint Vendor Technology Evaluation 2003: Summary of Results and Analysis Report , 2004 .

[4]  Anil K. Jain,et al.  Fingerprint Quality Indices for Predicting Authentication Performance , 2005, AVBPA.

[5]  John P. Baker,et al.  Fusion of Biometric Data with Quality Estimates via a Bayesian Belief Network , 2005 .

[6]  Anil K. Jain,et al.  A Principled Approach to Score Level Fusion in Multimodal Biometric Systems , 2005, AVBPA.

[7]  Wei-Yun Yau,et al.  Fusion of Auxiliary Information for Multi-modal Biometrics Authentication , 2004, ICBA.

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

[9]  Anil K. Jain,et al.  Combining multiple matchers for a high security fingerprint verification system , 1999, Pattern Recognit. Lett..

[10]  Anil K. Jain,et al.  Incorporating Image Quality in Multi-algorithm Fingerprint Verification , 2006, ICB.

[11]  John Daugman,et al.  10.7 – How Iris Recognition Works , 2005 .

[12]  Julian Fiérrez,et al.  Rapid and brief communication: Discriminative multimodal biometric authentication based on quality measures , 2005 .

[13]  Anil K. Jain,et al.  Localized Iris Image Quality Using 2-D Wavelets , 2006, ICB.

[14]  John Daugman.  How iris recognition works , 2004 .