Features Extraction and Recognition of Intersected Human Face Based on Wavelet Transform and KPCA
融合小波变换与KPCA的分块人脸特征抽取与识别算法

According to powerful compression and expression ability for high-dimensional image of wavelet multi-scale transformation,a feature extraction and recognition technology of intersected human face based on wavelet transform and KPCA(kernel principal component analysis) is proposed in this paper.With this method,the image of human faces are firstly divided into small different pieces which then being transformed with wavelet transformation algorithm.Secondly according to the positions of intersected small images coefficients of different frequency are chosen as extracted warelet features.Thirdly with KPCA the principal components of these features and then by combining these the ultiwate discriminate features are obtained.Finally the features are classified with the classifier of SVM(support vector machine).The experimental results on ORL and Yale face database show that the proposed method is superior to traditional PCA methods and KPCA methods with wavelet transformation,and it is also fairly robust to the variety of different illumination condition,face pose and expression.