Studies On PCA Human Face Recognition Based On Wacelet Transform And Neural Network

As the development of the society, there are increasing demands in automatic identity 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.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 wavelet transform and the artificial neural network for face recognition. In this method, we first preprocessed the face image to eliminate the effects of illumination difference on the images, then the wavelet transform was used to obtain the stable low frequency sub-band of the image in relatively low dimensions. 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 adaptability of the neural network to improve the recognition rate and the robustness of the algorithm to noises. Comparing with the traditional PCA algorithm, the method proposed here significantly decreases the complexity of the algorithm and the characteristics extracted can have a better representation of the differences among different faces, which results in a high recognition and a high robustness of the algorithm. Experimental results presented in this thesis verified that the proposed algorithm is accurate and effective.