A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometric
| Type of publication: | Idiap-RR |
| Citation: | Poh_04_fuse_all_rr |
| Number: | Idiap-RR-68-2004 |
| Year: | 2004 |
| Institution: | IDIAP |
| 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}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Crossref by |
Poh_04_fuse_all |
| Added by: | [UNK] |
| Total mark: | 0 |
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