Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/146341
DC Field | Value | |
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dc.title | Video-based face recognition using adaptive hidden Markov models | |
dc.contributor.author | Liu X. | |
dc.contributor.author | Chen T. | |
dc.date.accessioned | 2018-08-21T05:10:36Z | |
dc.date.available | 2018-08-21T05:10:36Z | |
dc.date.issued | 2003 | |
dc.identifier.citation | Liu X., Chen T. (2003). Video-based face recognition using adaptive hidden Markov models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1 : I/340-I/345. ScholarBank@NUS Repository. | |
dc.identifier.issn | 10636919 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/146341 | |
dc.description.abstract | While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently. In this paper, we propose to use adaptive Hidden Markov Models (HMM) to perform video-based face recognition. During the training process, the statistics of training video sequences of each subject, and the temporal dynamics, are learned by an HMM. During the recognition process, the temporal characteristics of the test video sequence are analyzed over time by the HMM corresponding to each subject. The likelihood scores provided by the HMMs are compared, and the highest score provides the identity of the test video sequence. Furthermore, with unsupervised learning, each HMM is adapted with the test video sequence, which results in better modeling over time. Based on extensive experiments with various databases, we show that the proposed algorithm provides better performance than using majority voting of image-based recognition results. | |
dc.source | Scopus | |
dc.type | Conference Paper | |
dc.contributor.department | OFFICE OF THE PROVOST | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.sourcetitle | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | |
dc.description.volume | 1 | |
dc.description.page | I/340-I/345 | |
dc.description.coden | PIVRE | |
dc.published.state | published | |
Appears in Collections: | Staff Publications |
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