Neural Net-Based Algorithms Comparison for Facial Gender Classification

The techniques of eigenfaces and neural net-based algorithms (LS-SVM and BP NNs) are combined to categorize gender from facial images in this paper. Based on exploration of the related techniques, the eigenfaces were firstly established from the training images, and the projection coefficients for training and testing images obtained in the space spanned by the eigenfaces; after that the LS-SVM and BP classifiers are built with training coefficients, which are used for classifying training and testing images, and classification accuracy percentage values are calculated. The experiments are implemented with our self-made facial images, and the results demonstrate that LS-SVM classification has better performance than BP . In experiments we also use cross validation to determine the number of selected primary components and kernel function parameter.