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Video-Based Face Recognition Using Adaptive Hidden Markov Models

9 years 5 months ago
Video-Based Face Recognition Using Adaptive Hidden Markov Models
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 videobased 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.
Xiaoming Liu 0002, Tsuhan Chen
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2003
Where CVPR
Authors Xiaoming Liu 0002, Tsuhan Chen
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