%Aigaion2 BibTeX export from Idiap Publications
%Tuesday 18 June 2024 11:51:59 AM

@INPROCEEDINGS{sby98e,
         author = {Ben-Yacoub, Souheil and Luettin, Juergen and Jonsson, K. and Matas, J. and Kittler, J.},
       projects = {Idiap},
          title = {Audio-Visual Person Verification},
      booktitle = {Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 1999, Fort Collins, USA},
           year = {1999},
           note = {IDIAP-RR 98-18},
       crossref = {sby98b},
       abstract = {In this paper we investigate benefits of classifier combination for a multimodal system for personal identity verification. The system uses frontal face images and speech. We show that a sophisticated fusion strategy enables the system to outperform its facial and vocal modules when taken seperately. We show that both trained linear weighted schemes and fusion by Support Vector Machine classifier leads to a significant reduction of total error rates. The complete system is tested on data from a publicly available audio-visual database according to a published protocol.},
            pdf = {https://publications.idiap.ch/attachments/reports/1998/rr98-18.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/1998/rr98-18.ps.gz},
ipdinar={1998},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{sby98b,
         author = {Ben-Yacoub, Souheil and Luettin, Juergen and Jonsson, K. and Matas, J. and Kittler, J.},
       projects = {Idiap},
          title = {Audio-Visual Person Verification},
           type = {Idiap-RR},
         number = {Idiap-RR-18-1998},
           year = {1998},
    institution = {IDIAP},
           note = {IEEE Proceedings of Computer Vision and Pattern Recognition 1999. Published in IEEE Proceedings of CVPR'99, Fort Collins, USA},
       abstract = {In this paper we investigate benefits of classifier combination for a multimodal system for personal identity verification. The system uses frontal face images and speech. We show that a sophisticated fusion strategy enables the system to outperform its facial and vocal modules when taken separately. We show that both trained linear weighted schemes and fusion by Support Vector Machine classifier leads to a significant reduction of total error rates. The complete system is tested on data from a publicly available audio-visual database according to a published protocol.},
            pdf = {https://publications.idiap.ch/attachments/reports/1998/rr98-18.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/1998/rr98-18.ps.gz},
ipdmembership={vision},
}