Small sample and pose variation are two critical technical problems in automatic face recognition (AFR). The combination of these two difficulties will seriously affect the performance of face recognition and restrict the wide application of AFR. To address this problem, we propose a new multi-feature fusion framework. First, we propose a pixel-level data augmentation algorithm based on manifold subspace partition, which constructs virtual samples in the original face image space to achieve training sample expansion and diversity enhancement. Then, we propose a feature-level data augmentation based on Gabor transformation, which can capture multi-level face features through multi-scale and multi-direction Gabor filters to realize the face expansion in feature space. To eliminate the data redundancy and interference information generated by Gabor feature augmentation, a Gabor feature encoding algorithm is…