Class-Dependent Feature Selection for Face Recognition

Feature extraction and feature selection are very important steps for face recognition. In this paper, we propose to use a class-dependent feature selection method to select different feature subsets for different classes after using principal component analysis to extract important information from face images. We then use the support vector machine (SVM) for classification. The experimental result shows that class-dependent feature selection can produce better classification accuracy with fewer features, compared with using the class-independent feature selection method.