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Research on Algorithms of Face Detection and Recognition across Cameras

In this paper, a cross-camera face detection and recognition system is proposed in the paper, which realizes the detection and recognition of the same face in different cameras as well as the dynamic face tracking. In the face detection, the method of gradually narrowing the detection range to achieve the final detection. The first step is to do a motion detection of the target and to detect the area of human body. Next step is to locate the human skin area through the skin color feature. The third step is to realize the face detection by AdaBoost algorithm. This method can not only improve the correct detection rate,but also effectively reduce the missed detection rate. In the face tracking,the Camshift algorithm is optimized to realize the real-time tracking of the human face, and Kalman prediction mechanism is applied in the algorithm, thus improving the tracking effect of the obscured target. Experiments show that the tracking of the target can be more accurate by the combination of the two methods. In the face recognition, the paper combines Gabor wavelet and PCA to fulfil the recognition task of face image. The combination of the two algorithms can effectively extract the features of the face image and reduce the dimension of the feature data as well as simplify calculation significantly.