Abstract Discriminative facial features can be obtained by deep learning model. Therefore, combining the deep learning, a multi-level deep network extraction model for fusion feature is proposed. In the proposed model, the pooling layer is added after subspace mapping based on deep subspace model, so that the feature dimension is reduced with texture details preserving and local transformation robustness.Meanwhile, face region is divided into 5 parts according to facial feature point achieved by face alignment algorithm. Based on multi-level classification strategy, the global network is firstly trained using the whole face image to obtain five candidate labels for test sample. Then, the local face block is put into sub-network to obtain local representation and test samples are classified in the candidate labels. Experimental results show that the model combined with the local features and global features achieves better accuracy and robustness in the aspect of the illumination, expression, occlusion, etc. Moreover, adding pooling structure and the two-step discrimination algorithm effectively improve the recognition efficiency.
About author:: (HU Zhengping(Corresponding author), born in 1970, Ph.D., professor. His research interests include pattern recognition.) (HE Wei, born in 1993, master student. Her research interests include deep lear-ning.) (WANG Meng, born in 1982, Ph.D. candidate. Her research interests include pattern recognition.) (SUN Zhe, born in 1990, Ph.D. candidate. Her research interests include sparse representation classification.)
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2017, Vol. 30 
