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Information fusion of multi-biometrics has become a center of focus for biometrics based identification and verification, and there are two fusion categories: intra-modal fusion and multi-modal fusion. In this paper, an intra-modal fusion, that is, multi-parts and multi-feature fusion (MPMFF) for face verification is studied. Two face representations are exploited, including the gray-level intensity feature and Gabor feature. Different from most face recognition methods, the MPMFF method divides a face image into five parts: bare face, eyebrows, eyes, nose and mouth, and different features of the same face part are fused at feature level. Then at decision level, five matching results based on the combined-features of different parts are calculated into a final similar score according to the weighted sum rule. Experiment results on FERET face database and our own face database show that the multi-parts and multi-feature fusion method improves the face verification performance.