2016 IEEE International Conference on Multimedia and Expo (ICME) (2016)
Seattle, WA, USA
July 11, 2016 to July 15, 2016
Zhongjun Wu , Beijing University of Posts and Telecommunications, No. 10, Xitu Cheng Road, Haidian District, Beijing, China, 100876
Weihong Deng , Beijing University of Posts and Telecommunications, No. 10, Xitu Cheng Road, Haidian District, Beijing, China, 100876
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.
Lighting, Face, Three-dimensional displays, Shape, Solid modeling, Face recognition, Image reconstruction
Z. Wu and W. Deng, "One-shot deep neural network for pose and illumination normalization face recognition," 2016 IEEE International Conference on Multimedia and Expo (ICME), Seattle, WA, USA, 2016, pp. 1-6.