A face verification method is presented in this chapter by fusing the frequency and color features for improving the face recognition grand challenge performance. In particular, the hybrid color space
is constructed, according to the discriminating properties among the individual component images. For each component image, the frequency features are extracted from the magnitude, the real and imaginary parts in the frequency domain of an image. Then, an improved Fisher model extracts discriminating features from the frequency data for similarity computation using a cosine similarity measure. Finally, the similarity scores from the three component images in the
color space are fused by means of a weighted summation at the decision level for the overall similarity computation. To alleviate the effect of illumination variations, an illumination normalization procedure is applied to the
component image. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show the feasibility of the proposed frequency and color fusion method.