Deformable Face Net: Learning Pose Invariant Feature with Pose Aware Feature Alignment for Face Recognition.

Face recognition plays an important role in computer vision. It still remains a challenging task due to pose, expression, illumination, partial occlusion, etc. In this work, we propose a novel Deformable Face Net (DFN) to handle the pose variations in face recognition. The Deformable Face Net introduces deformable convolution modules to simultaneously learn face recognition oriented alignment and feature extraction. Specifically, two loss functions, namely displacement consistency loss (DCL) and identity consistency loss (ICL) are designed to minimize the intra-class feature variation caused by different poses. These two loss functions jointly learn pose-aware displacement fields for deformable convolutions in the DFN. Different from the existing methods, the DFN focuses on aligning features across different poses rather than frontalizing the input faces. Extensive experiments show that the proposed DFN outperforms the state-of-the-art methods, especially on the datasets with large poses.