Studies On Face Recognition Method Based On PCA And Neural Networks

As the development of the society, there are increasing demands in automatic identify check. Since some biological characteristics are intrinsic and stable to people and are strongly different from one to the others, they can be used as features for identity check. Among all the characteristics of human, the characteristics of face are the most direct tools which are friendly and convenient and can easily be accepted by the customers.Learned essays, research papers the domestic and international in recent years concerning person's face recognition, some theories problem to computer identify technique to person's face is analyzed. Aim at to establish the personal identification system of the main technique, face recognition to proceeds the in-depth researched, and bring upped a method for used for frontal face recognition in video images.Face recognition is an extensive and challenging research problem. Recently, significant progresses have been made in the technology of the face recognition. In this thesis, we first introduced the background and the main methods of the face recognition and then proposed a method based on the artifical neural network for face recognition. First, in order to eliminate the correlations between entities of the image vector, the principal component analysis (PCA) was used. After the features of the images was extracted, an improved back propagation (BP) algorithm was introduced to train the neural network for recognition. This algorithm combines the optimization of the PCA and the adaptabiltyof the neural network to improve the recognition rate and the robustness of the algorithm to noises. Experimental results presented in this thesis verifited that the proposed algorithm is accurate and effective.