Visual Tracking and face recognition play important roles in visual analysis and applications. Tracking can provide object information like location and gesture which found the base of higher level visual processing tasks, such as recognition and control decision. Although tracking is widely studied in the computer vision field, it still remains a challenging problem in methodology and practice, especially in the cases of illumination changes, motion blurring, sensor noise and occlusion. Face recognition is another important problem in robot human computer interaction. With the wide distribution of various kinds of sensors, e.g. infrared camera and 3D camera, multiple modal images can be captured conveniently nowadays. Therefore, how to utilize multi-model image to boost up face recognition becomes an attractive research topic. This thesis focuses on the problem of robust object tracking and face recognition. Specifically, we mainly discuss the following four topics in detail: (1)Online object appearance modeling based on manifold learning; (2) Tracking by co-training with multiple support vector machines; (3) Multiple faces tracking system and (4) Multi-modal face recognition. The main contributions of this thesis include: 1 We proposed a novel incremental Laplacian Eigenmaps algorithm and apply it to online object appearance modeling. Experimental results on video clips data demonstrate the effectiveness of our method. Compared with state-of-the-art appearance modeling method, our proposed method achieves higher accuracy and robustness. 2 We proposed a tracking framework based on support vector machine co-training. We redefine the object tracking problem in the setting of semi-supervised learning. The individual support vector tracker built upon color, texture and gradient feature are combined and updated by co-training method. The proposed algorithm is tested on many video clips, ranging from internet video, infrared clips and clips captured from mobile platform. Experimental results show the robustness of our proposed algorithm, especially under the situation of drastic appearance changes. 3 We developed a multiple faces tracking framework based on interaction between face detection and face tracking. This framework reaps the efficient of face detection and effective of tracking algorithm. Experimental results show that proposed algorithm can robustly track multiple faces in real-time. 4 We applied, for the first time, the Local Binary Pattern...
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