Face Recognition Based On Wavelet Transform And PCA

Automatic face recognition technology is analyzes the person face picture using the computer, extract the effective identification information, uses for identify the status of a technology. It is a totally brand-new technique different from traditional methods because it adopts the inherent organism's characteristics of human body. More and more people pay great attention to it as it is safer, more reliable and effective. Face recognition consists of three parts: Preprocessing, feature extraction and classification. In this thesis, we use histogram equalization to modify the picture and use wavelet transform to reduce the dimension of picture in the processing. Then use PCA extract feature and finally use distance classification to get the result of recognition.As the most successful method of linear differential, principal component analysis (PCA) method is widely used in identify areas, such as face recognition. But traditional PCA is influenced by the light conditions and facial expression changed because it extract the global feature of the image, so the recognition effect is not very good. We know when expression and light conditions changing, only some face region obvious changed, and the other changed is not too much, even no changed. So based on this situation, this thesis presents a method of block-PCA. In this method, we first segment the picture and then extract eigenvector using the PCA. In extracting feature, for each of the parts in accordance with the different role in the overall image give the different weights. In the finally, we construct classification and get the result of recognition. When we use PCA method to extract feature, we should convert the image to one-dimensional vector, so it make the vector dimension of image too much high and induce difficulty to extract feature. Because of this, this thesis presents another method, it is block-2DPCA. After we segmented the image, we use 2DPCA to extract feature in the sub-image space. The matrix dimension of 2DPCA is far below dimension of the covariance matrix using PCA, because it uses the two-dimensional matrix of original image directly to make the covariance matrix. So is can be greatly improved the speed and accuracy, reduce the complexity of feature extraction. In the finally, it improved the speed of recognition and the recognition rate.In the finally, the experimental result show the improved method of PCA is superior to method traditional PCA.