Feature selection in frequency domain and its application to face recognition

Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms (GAs) as well as dimensionality reduction methods are utilized to create a combined framework of feature acquisition. In details, the face images are first transformed to frequency domain through DCT. Then GAs are used to seek for optimal features in the redundant DCT coefficients where the generalization performance guides the searching process. The last step is to reduce the dimension of selected features. In experiments, two face databases are used to evaluate the effectiveness of the proposed method. In addition, an entropy-based improvement is also proposed. The experimental results present the superiority of selected frequency features.