Improving identification performance by integrating evidence from sequences

G. J. Edwards, C. J. Taylor, T. F. Cootes

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.
    Original languageEnglish
    Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit
    Place of PublicationLos Alamitos, CA, United States
    PublisherIEEE
    Pages486-491
    Number of pages5
    Volume1
    Publication statusPublished - 1999
    EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
    Duration: 1 Jul 1999 → …

    Conference

    ConferenceProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99)
    CityFort Collins, CO, USA
    Period1/07/99 → …

    Keywords

    • Peer Reviewed Conference

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