Improving Fusion with Margin-Derived Confidence in Biometric Authentication Tasks

This study investigates a new confidence criterion to improve fusion via a linear combination of scores of several biometric authentication systems. This confidence is based on the margin of making a decision, which answers the question, “after observing the score of a given system, what is the confidence (or risk) associated to that given access?”. In the context of multimodal and intramodal fusion, such information proves valuable because the margin information can determine which of the systems should be given higher weights. Finally, we propose a linear discriminative framework to fuse the margin information with an existing global fusion function. The results of 32 fusion experiments carried out on the XM2VTS multimodal database show that fusion using margin (product of margin and expert opinion) is superior over fusion without the margin information (i.e., the original expert opinion). Furthermore, combining both sources of information increases fusion performance further.

[1]  Kuldip K. Paliwal,et al.  Noise compensation in a person verification system using face and multiple speech feature , 2003, Pattern Recognit..

[2]  Hong Yan,et al.  Comparison of face verification results on the XM2VTFS database , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Samy Bengio,et al.  The expected performance curve: a new assessment measure for person authentication , 2004, Odyssey.

[4]  Alfred C. Weaver,et al.  Biometric authentication , 2006, Computer.

[5]  Julian Fiérrez,et al.  On the use of quality measures for text-independent speaker recognition , 2004, Odyssey.

[6]  Julian Fiérrez,et al.  Multimodal biometric authentication using quality signals in mobile communications , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[7]  Gérard Chollet,et al.  Multi-modal identity verification using expert fusion , 2000, Inf. Fusion.

[8]  Xudong Jiang,et al.  A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Alvin F. Martin,et al.  The DET curve in assessment of detection task performance , 1997, EUROSPEECH.

[10]  H. Chandler.  Database , 1985 .

[11]  Javier Ortega-Garcia,et al.  Kernel-based multimodal biometric verification using quality signals , 2004, SPIE Defense + Commercial Sensing.

[12]  Sridha Sridharan,et al.  Robust speaker verification via fusion of speech and lip modalities , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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

[15]  Samy Bengio,et al.  A statistical significance test for person authentication , 2004, Odyssey.

[16]  Samy Bengio,et al.  Non-Linear Variance Reduction Techniques in Biometric Authentication , 2003 .

[17]  Samy Bengio,et al.  Database, protocols and tools for evaluating score-level fusion algorithms in biometric authentication , 2006, Pattern Recognit..

[18]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

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