Improving identification performance by integrating evidence from sequences

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.

Bibliographical metadata

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
Event 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 → …