Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A System Identification Approach for Video-based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
An online-optimized incremental learning framework for video semantic classification
Proceedings of the 12th annual ACM international conference on Multimedia
Handbook of Face Recognition
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Online learning is a very desirable capability for videobased algorithms. In this paper, we propose a novel framework to solve the problems of video-based face tracking and recognition by online updating twin GMMs. At first, considering differences between the tasks of face tracking and face recognition, the twin GMMs are initialized with different rules for tracking and recognition purposes, respectively. Then, given training sequences for learning, both of them are updated with some online incremental learning algorithm, so the tracking performance is improved and the class-specific GMMs are obtained. Lastly, Bayesian inference is incorporated into the recognition framework to accumulate the temporal information in video. Experiments have demonstrated that the algorithm can achieve better performance than some well-known methods.