Face detection and tracking using a boosted adaptive particle filter
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This thesis proposes a novel algorithm for integrated face detection and face tracking based on the synthesis of an adaptive particle filtering algorithm and an AdaBoost face detection algorithm. A novel Adaptive Particle Filter (APF), based on a new sampling technique, is proposed to obtain accurate estimation of the proposal distribution and the posterior distribution for accurate tracking in video sequences. The proposed scheme, termed a Boosted Adaptive Particle Filter (BAPF), combines the APF with the AdaBoost algorithm. The AdaBoost algorithm is used to detect faces in input image frames, while the APF algorithm is designed to track faces in video sequences. The proposed BAPF algorithm is employed for face detection, face verification, and face tracking in video sequences. Experimental results confirm that the proposed BAPF algorithm provides a means for robust face detection and accurate face tracking under various tracking scenarios.