Research And Applications Of Face Recognition Method Based On Image Analysis

Along with the rapid development of biometric technology, face recognition technology has become a hot research spot of domestic and international security. In applications, the face recognition system need to use face detection algorithm to capture face images, it will produce light and attitude change, and leading to the recognition result unstable. Adaboost algorithm is a method of face detection technology based on machine learning, and the PCA algorithm is a classic algorithm of face recognition technology. In this paper, we study two kinds of face detection algorithm and a variety of face recognition algorithm, proposed TSPCA face recognition algorithm. The TSPCA algorithm uses the PHM algorithm to normalized face images, in order to reduce the impact of light change. Then, TS algorithm is used to extract texture spectrum features of the face images, PCA algorithm is choose to reduce the dimension of the features. At last, doing face recognition in the low-dimension space. Experimental results shows that compared with the traditional face recognition algorithm, TSPCA algorithm has higher recognition rate and faster recognition speed. Meanwhile, this paper set a real-time face recognition system, the system has five different face recognition algorithm, including the TSPCA algorithm. First, the system uses the Adaboost algorithm to detect the face area. Then, PHM algorithm is used to normalize the face images. At last, researcher choose the face recognition algorithm to recognize the face. In the end of this paper, the experiments of the algorithm’s recognition rate and the recognize speed is given. The experimental results shows that compared with the traditional face recognition algorithm, the TSPCA algorithm has higher recognition rate and faster recognition speed.