Abstract An algorithm of sparse feature extraction model is proposed based on deep and symmetric subspace learning. According to the theory of deep subspace learning, the constraints of symmetry and sparsity are introduced and the deep map network is built to extract features. Firstly, the basic subspace mapping matrix is constructed by minimizing the reconstruction error and the constraints of symmetry and sparsity are introduced for training. Next, the basic subspace model based on deep learning is reformed, and the deep symmetric sparse feature extraction model is built. These feature extraction results from different layers are merged to obtain the multi-layered deep symmetric subspace sparse feature. The experimental results on face databases show that the proposed algorithm achieves high recognition rates and strong robustness in illumination, expression and pose. Furthermore, compared with the convolutional neural networks, the proposed algorithm has the advantages of simple structure and high convergent rate.
Fund:Supported by National Natural Science Foundation of China(No.61071199), Natural Science Foundation of Hebei Province(No.F2016203422)
Corresponding Authors:
(HU Zhengping(Corresponding author), born in 1970, Ph.D., professor. His research interests include pattern recognition.)
About author:: (HU Zhengping(Corresponding author), born in 1970, Ph.D., professor. His research interests include pattern recognition.) (CHEN Junling, born in 1991, master student. His research interests include deep learning.) (WANG Meng, born in 1982, Ph.D. candidate, lecturer. Her research interests include pattern recognition.) (SUN Zhe, born in 1990, Ph.D. candidate. Her research interests include pattern recognition.)
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2017, Vol. 30 
