This chapter presents a Gabor-DCT Features (GDF) method on color facial parts for face recognition. The novelty of the GDF method is fourfold. First, four discriminative facial parts are used for dealing with image variations. Second, the Gabor filtered images of each facial part are grouped together based on adjacent scales and orientations to form a Multiple Scale and Multiple Orientation Gabor Image Representation (MSMO-GIR). Third, each MSMO-GIR first undergoes Discrete Cosine Transform (DCT) with frequency domain masking for dimensionality and redundancy reduction, and then is subject to discriminant analysis for extracting the Gabor-DCT features. Finally, at the decision level, the similarity scores derived from all the facial parts as well as from the Gabor filtered whole face image are fused together by means of the sum rule. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 and the CMU Multi-PIE database show the feasibility of the proposed GDF method.