Video-based Multi-view Face Detection And Tracking

Multi-view face detection and tracking is a significant aspect in computer vision and pattern recognition research. It is the base of face information processing and plays an important role in face recognition, man-machine interaction, video conference, 3-G mobile communication.This thesis mainly study on key technologies of video-based multi-view face detection and tracking, including video pre-process technique, multi-view face detection algorithm, face image standardization, multi-view face tracking and the realization of surveillance face recognition system.The main contribution of this dissertation is listed as follows.(1). Video preprocess algorithm including light compensation, image de-noise and super-resolution algorithm to satisfy the demand of real-time video and surveillance system.(2). Based on the existent face detection algorithm, the author realizes multi-view face detection and proposes face image validation and re-location algorithm using face features and color model.(3). This paper proposes an innovative algorithm based on sub-space model for robust multi-view face tracking under complex environment. It combines the offline face models and online self-learning models. We also present a new self-adaptive particle filter algorithm to track face. Experiments demonstrate that our algorithm can handle multi-view and multi-scale face detection and tracking steadily in complex environment.(4). This paper studies and realize the best frontal face choosing algorithm, including wavelet-based image evaluation algorithm, pose estimation using subspace dimensional reduction algorithm, face choosing algorithm using distance and SVM-based face classification. A novel method for selection of high-resolution image of interesting area is proposed and a surveillance face recognition system based on multi-task fusion programming is also developed.