%Aigaion2 BibTeX export from Idiap Publications
%Thursday 04 December 2025 03:41:15 PM

@INPROCEEDINGS{Poh_04_fuse_all,
                      author = {Poh, Norman and Bengio, Samy},
                    projects = {Idiap},
                       title = {A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometric},
                   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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2005/norman-2005-AVBPA-pki.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2005/norman-2005-AVBPA-pki.ps.gz},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{Poh_04_fuse_all_rr,
                      author = {Poh, Norman and Bengio, Samy},
                    projects = {Idiap},
                       title = {A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometric},
                        type = {Idiap-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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2004/rr04-68.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2004/rr04-68.ps.gz},
ipdmembership={learning},
}