Face recognition system usually consists of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification positively because of the variations of illumination and poses in face images. In this paper, an evolutionary feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms (GAs) are utilized to create a framework of feature acquisition. In detail, 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. Furthermore, an entropy-based extension on proposed evolving feature selection method is presented. In experiments, two face databases are used to evaluate the effectiveness of our proposals.