Face recognition using multi-scale PCA and Support Vector Machine

Based on Gabor wavelets, a novel multi-scale principal component analysis and support vector machine algorithm (MsPCA-SVM) for face recognition is proposed in this paper. Firstly, the Gabor wavelets transformation results including five scales and eight directions are calculated and 40 feature matrices which are reconstructed with the same scale and the same direction transform results of the different face images are obtained. Secondly, the dimensionality reduction and denoised technique with PCA are applied to form the new training samples. Finally, 40 SVMs classifiers are constructed and the vote decision strategy is used to determine the recognition results. The experimental results show that the proposed method expands the selectable range of the cumulative variance contribution rate in PCA method and the influence of the SVMs kernel parameters on the recognition rate is small. So, the SVMs kernel parameters are easy to select. Furthermore, the difficult problem to select the kernel parameters has been settled to a certain degree. In the meantime, the ideal recognition rate is obtained.