Abstract: Taking the ORL Faces database of the computer laboratory of Cambridge University as the experimental data,the experimental data are processed through the "convolution-pooling" layer in the convolution neural network,and the LIBSVM integration software is selected as the tool to classify and identify the original data and the data processed by "convolution-pooling".SVM parameters are combined with C-SVC model,nu-SVC model and linear kernel function,polynomial kernel function,radial basis kernel function and Sigmoid kernel function.The convolution-pooling processing can achieve data dimensionality reduction and "gaussian smooth convolution kernel convolution-pooling" processing can improve SVM face recognition rate.SVM is more suitable to choose C-SVC model linear kernel function in face recognition.

Key words: face recognition, convolution, pool, SVM