Performance Analysis for Integrating Biometrics

Due to the performance limitation of using a single biometric, many researchers have been investigating ways to integrate multiple biometrics for the purpose of authentication. To this end, there have been numerous articles on promoting multiple biometric identifiers. It is thus important to know when integrating improves the performance and when it does not. This research focuses on integrating biometrics at the decision level. In this article, the formulas are first derived for the probabilities of a False Accept and a False Reject when n Biometric tests are combined. The conditions are then obtained for performance improvement and degradation of combing two biometric identifiers. To the end, some other interesting results of integrating biometric solutions are also presented.

[1]  Slobodan Ribarić,et al.  Multimodal biometric user-identification system for network-based applications , 2003 .

[2]  Josef Kittler,et al.  Fusion of multiple experts in multimodal biometric personal identity verification systems , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[3]  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.

[4]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[5]  Anil K. Jain,et al.  Integrating Faces and Fingerprints for Personal Identification , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Xudong Jiang,et al.  Exploiting global and local decisions for multimodal biometrics verification , 2004, IEEE Transactions on Signal Processing.

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

[8]  Quoc-Long Tran,et al.  Adaptation to changes in multimodal biometric authentication , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

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

[10]  Luhong Liang,et al.  Environment-adaptive multi-channel biometrics , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[11]  Slobodan Ribaric,et al.  A biometric identification system based on eigenpalm and eigenfinger features , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  L. Hong,et al.  Can multibiometrics improve performance , 1999 .

[13]  F. Deravi,et al.  Towards optimised implementations of multimodal biometric configurations , 2004, Proceedings of the 2004 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2004. CIHSPS 2004..

[14]  Anil K. Jain,et al.  Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Guang-Da Su,et al.  Information fusion in face and fingerprint identity verification system , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[16]  Robert Frischholz,et al.  BioID: A Multimodal Biometric Identification System , 2000, Computer.

[17]  Kar-Ann Toh Personalized learning and decision for multimodal biometrics , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[18]  Sun-Yuan Kung,et al.  A two-level fusion approach to multimodal biometric verification , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[19]  Julian Fiérrez,et al.  Fusion strategies in multimodal biometric verification , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).