A Boosted Adaptive Particle Filter for Face Detection and Tracking

Abstract

A novel algorithm, termed a boosted adaptive particle filter (BAPF), for integrated face detection and face tracking is proposed. The proposed algorithm is 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 estimates of the proposal distribution and the posterior distribution to enable accurate tracking in video sequences. 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 show that the proposed BAPF algorithm provides a means for robust face detection and accurate face tracking under various tracking scenarios.