Research On The Technique Of Face Recognition

Over the last ten years, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. Because of the nature of the problem, not only computer science researchers are interested in it, but neuroscientists and psychologists also. It is the general opinion that advances in computer vision research will provide useful insights to neuroscientists and psychologists into how human brain works, and vice versa.A general statement of the face recognition problem (in computer vision) can be formulated as follows: Given still or video images of a scene, identify or verify one or more persons in the scene using a stored database of faces. Although a great deal of effort has been devoted to 2D intensity image based face recognition task, it will still remain a challenging problem in a general setting. Successful 2D face recognition systems have been developed only under constrained situations. One major factor limiting the applications of 2D face recognition systems is that human face image appearance has potentially very large intra-subject variations due to 3D head pose, illumination, and facial expression. On the other hand, the inter-subject variations can be small due to the similarity of individual appearances.Face Recognition is a user-friendly technique for personal identification, which is regarded as an important direction of biometric recognition techniques. It is a complex and difficult problem that is important for surveillance and security, telecommunications, digital libraries, video meeting, and human-computer intelligent interactions. Despite the fact that human faces are essentially similar, we are very skilled at recognizing the identities of people from their faces. We can perform this task very easily and it is a basic and important social act although we are still puzzled with the psychological and physiological nature of the process.Four face recognition technologies are presented in the research. Based on the ORL and Yale face database five kinds of face recognition technology, including PCA features, LDA features and HMM, are used. The recognition performances of these technologies are tested by using minimum Euclidean distance classifier. The experimental results show that the two dimensional method achieve the better recognition performance, and the combining feature outperform the single feature.