Face Recognition Algorithm, Singular Value Decomposition

With the development of information technology, social management tends to be electricized and automated. In such a large social network system, system security is of vital importance with the premise to make out personal identification. As face is an important part for identification, it is most direct and natural to identify a person by means of face features. This kind of way is characterized by convenience and friendliness compared with other means and will be easily accepted by without stirring up defense. Naturally, face identification has bright application future in the field of criminal distinguishing, safety check system, credit card identification, medicine, file management, video meeting, people-machine exchange system, and will become a hotspot in the recent research of pattern recognition and artificial intelligence.Face Recognition(FR), having aroused great interest of the researchers in the early 1960's, involves pattern recognition, image process, physiology, psychology, recognition science and so on. It can be separated into three parts: face detection and orientation, feature extraction and recognition.In the part of face detection and orientation, vertical orientation will be made according to inner outline within edge extraction picture of the face image. After that, according to the horizontal level features of human eye, face and human eyes will be horizontal orientated in the gray image. Then, the binary image of the eye will be cut in accordance with the eye horizontal level, which will be vertically projected to orientate the accurate position of human eyes. The human eyes will in turn accurately position the face to further get standard image. Experiments prove the accuracy reaches to 88.6%.Face feature extraction will apply the method based on singular value decomposition which takes singular value decomposition of the matrix as feature vector.A distance classifier has been built up for face recognition. If Y is an image to be recognized, the feature vector Y will first be worked out, and the feature vector center of i sample class will be noted as Xt. Then the distance between those two vectors can be calculated for judgements. We classify the image into the sample class that has the shortest distance. Such kind of method has proved 76% accuracy.