Balancing performance factors in multisource biometric processing platforms

It is generally recognised that no one biometric data source or processing platform is universally appropriate for optimising performance across all problem domains. Multibiometric processors, which combine identity information obtained from more than one biometric source are commonly promoted as optimal structures for maximising performance, and much research has been carried out to investigate appropriate strategies for combining the available information. However, the techniques of multiclassifier pattern recognition also offer opportunities to improve the performance of systems operating within a unimodal environment, yet such solutions have been less extensively investigated in the specific case of biometric applications. This study presents an empirical study of the relations between these two different approaches to enhancing the performance indicators delivered by biometric systems. In particular we are interested to increase our understanding of the relative merits of, on the one hand, multiclassifier/single modality systems and, on the other, full multibiometric configurations. We focus our study on three modalities, the fingerprint and hand geometry (two physiological biometrics) and the handwritten signature (a behavioural biometric).

[1]  Martin D. Buhmann,et al.  Radial Basis Functions , 2021, Encyclopedia of Mathematical Geosciences.

[2]  Miroslav Kubat,et al.  Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. , 1999, The Knowledge Engineering Review.

[3]  Julian Fiérrez,et al.  Combining multiple matchers for fingerprint verification: A case study in biosecure network of excellence , 2007, Ann. des Télécommunications.

[4]  Richard M. Guest.  Age dependency in handwritten dynamic signature verification systems , 2006, Pattern Recognit. Lett..

[5]  Luis A. Hernández Gómez,et al.  Usability evaluation of multi-modal biometric verification systems , 2006, Interact. Comput..

[6]  Richard M. Guest.  The repeatability of signatures , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[7]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[8]  Michael C. Fairhurst,et al.  Biosecure reference systems for on-line signature verification: A study of complementarity , 2007, Ann. des Télécommunications.

[9]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[12]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[13]  H. B. Kekre,et al.  Multimodal biometrics , 2010, ICWET.

[14]  Sunil Arya,et al.  An optimal algorithm for approximate nearest neighbor searching fixed dimensions , 1998, JACM.

[15]  Kevin W. Bowyer,et al.  Combination of Multiple Classifiers Using Local Accuracy Estimates , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[17]  Mircea Nicolescu,et al.  Peg-Free Hand Shape Verification Using High Order Zernike Moments , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[18]  Krzysztof Kryszczuk,et al.  Reliability-Based Decision Fusion in Multimodal Biometric Verification Systems , 2007, EURASIP J. Adv. Signal Process..

[19]  Arun Ross,et al.  Multimodal biometrics: An overview , 2004, 2004 12th European Signal Processing Conference.

[20]  Alan Mink,et al.  Multimodal biometrics: issues in design and testing , 2003, ICMI '03.

[21]  Sharath Pankanti,et al.  Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.

[22]  Anil K. Jain,et al.  Biometric Template Security , 2008, EURASIP J. Adv. Signal Process..