A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometricactive
Norman Poh and Samy Bengio
In: Audio- and Video-based Biometric Person Authentication (AVBPA2005), 20-22 July 2005, New York.
The issues of fusion with client-dependent and confidence information have been well studied separately in biometric authentication. In this study, we propose to take advantage of both sources of information in a discriminative framework. Initially, each source of information is processed on a per expert basis (plus on a per client basis for the first information and on a per example basis for the second information). Then, both sources of information are combined using a second-level classifier, across different experts. Although the formulation of such two-step solution is not new, the novelty lies in the way the sources of prior knowledge are incorporated prior to fusion using the second-level classifier. Because these two sources of information are of very different nature, one often needs to devise special algorithms to combine both information sources. Our framework that we call ``Prior Knowledge Incorporation'' has the advantage of using the standard machine learning algorithms. Based on 10x32=320 intramodal and multimodal fusion experiments carried out on the publicly available XM2VTS score-level fusion benchmark database, it is found that the generalisation performance of combining both information sources improves over using either or none of them, thus achieving a new state-of-the-art performance on this database.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Additional Information:||biometric authentication fusion|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Norman Poh|
|Deposited On:||02 January 2005|