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A Face Tracking Algorithm Based on Multiple Feature Mean Shift |
Zhang Tao;Cai Can-hui |
Institute of Information Science and Technology, Huaqiao University, Quanzhou 362021, China |
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Abstract In this paper, an improved Mean Shift face tracking algorithm based on Local Ternary Patterns (LTP) of texture and color features is proposed to improve the robustness of the Mean Shift algorithm. Based on the study of LTP texture features, an LTP key texture pattern is introduced to enhance the important features of an object and reduce the computational complexity of the LPT texture model. A multiple feature Mean Shift face tracking algorithm is then proposed based on the LTP key texture and complexion features, and the robustness of Mean Shift algorithm is significantly enhanced. Furthermore, in order to improve the tracking speed and robustness, the Kalman filter is introduced to predict the position of the object window. Experimental results show that compared with the original Mean Shift algorithm, the proposed multiple feature face tracking algorithm has significantly improved the tracking performance.
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Received: 04 September 2008
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