Zdenek Kalal, Krystian Mikolajczyk, and Jirí Matas. Face-TLD: Tracking-learning-detection applied to faces. In Bonnie Law, editor, 17th IEEE International Conference on Image Processing (ICIP'2010), pages 3789-3792, 445 Hoes Lane, Piscataway, USA, September 2010. IEEE Signal Processing Society.
A novel system for long-term tracking of a human face in unconstrained videos is built on Tracking-Learning-Detection (TLD) approach. The system extends TLD with the concept of a generic detector and a validator which is designed for real-time face tracking resistent to occlusions and appearance changes. The off-line trained detector localizes frontal faces and the online trained validator decides which faces correspond to the tracked subject. Several strategies for building the validator during tracking are quantitatively evaluated. The system is validated on a sitcom episode (23 min.) and a surveillance (8 min.) video. In both cases the system detects-tracks the face and automatically learns a multi-view model from a single frontal example and an unlabeled video.
