One-shot deep neural network for pose and illumination normalization face recognition

Pose and illumination are considered as two main challenges that face recognition system encounters. In this paper, we consider face recognition problem across pose and illumination variations, given small amount of training samples and single sample per gallery (a.k.a., one shot classification). We combine the strength of 3D models in generating multiviews and various illumination samples and the ability of deep learning in learning non-linear transformation, which is very suitable for pose and illumination normalization, by using a multi-task deep neural network. By the pose and illumination augmentation strategy, we train a pose and illumination normalization neural network with much less training data compared to other methods. Experiments on MultiPIE database achieve competitive recognition results, demonstrating the effectiveness of proposed method.