Pseudo Facial Generation With Extreme Poses for Face Recognition

Face recognition has achieved a great success in recent years, it is still challenging to recognize those facial images with extreme poses. Traditional methods consider it as a domain gap problem. Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity information with high computational consumption and uncontrolled disturbances. Our experimental analysis shows a dramatic precision drop with extreme poses. Meanwhile, those extreme poses just exist minor visual differences after small rotations. Derived from this insight, we attempt to relieve such a huge precision drop by making minor changes to the input images without modifying existing discriminators. A novel lightweight pseudo facial generation is proposed to relieve the problem of extreme poses without generating any frontal facial image. It can depict the facial contour information and make appropriate modifications to preserve the critical identity information. Specifically, the proposed method reconstructs pseudo profile faces by minimizing the pixel-wise differences with original profile faces and maintaining the identity consistent information from their corresponding frontal faces simultaneously. The proposed framework can improve existing discriminators and obtain a great promotion on several benchmark datasets.