Two-step Learning of Deep Convolutional Neural Network for Discriminative Face Recognition under Varying Illumination
Yeoreum Choi, Hyung-Il Kim, Yong Man Ro · Electronic Imaging · 2016
In real-world face recognition (FR) scenario, illumination variation has been known to be a challenging problem because face appearance dramatically changes depending on the illumination conditions. In order to deal with this illumination variation effectively, an illumination-reduced feature learning method using deep convolutional neural network (DCNN) is proposed in this paper. It is motivated by the capability of deep learning that represents highly complicated nonlinear structures. Our learning method is mainly comprised of following two-steps: 1) learning illumination patterns for eliminating illumination effect and 2) learning for maximizing discriminative power of feature representation. Experimental results on CMU Multi-PIE database have demonstrated that the proposed method outperforms the previous works in terms of FR accuracy.