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 [BibTeX] [Marc21]
A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometric
Type of publication: Conference paper
Citation: Poh_04_fuse_all
Booktitle: Fifth Int'l. Conf. Audio- and Video-Based Biometric Person Authentication AVBPA
Year: 2005
Crossref: poh_04_fuse_all_rr:
Abstract: 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 $10 \times 32=320$ 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.
Userfields: ipdmembership={learning},
Projects Idiap
Authors Poh, Norman
Bengio, Samy
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Total mark: 0
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