A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robust Real-Time Face Detection
International Journal of Computer Vision
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection and tracking in video sequences using the modifiedcensus transformation
Image and Vision Computing
Dynamic context capture and distributed video arrays for intelligent spaces
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Face detection and tracking in a video by propagating detection probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Challenges of human behavior understanding
HBU'10 Proceedings of the First international conference on Human behavior understanding
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We propose a method that tracks and recognizes faces simultaneously. In previous methods, features needed to be extracted twice for tracking and recognizing faces in image sequences because the features used for face recognition are different from those used for face tracking. To reduce the computational cost, we propose a probabilistic model for face tracking and recognition and a system that performs face tracking and recognition simultaneously using the same features. The probabilistic model handles any overlap in the camera's field of view, something that is ignored in previous methods. The model thus deals with face tracking and recognition using multiple overlapping image sequences. Experimental results show that the proposed method can track and recognize multiple faces simultaneously.