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Journal of Electrical and Computer Engineering
Volume 2018, Article ID 2179049, 11 pages
Research Article

Laplace Graph Embedding Class Specific Dictionary Learning for Face Recognition

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China

Correspondence should be addressed to Yan-Jiang Wang; nc.ude.cpu@gnawjy

Received 23 September 2017; Revised 2 December 2017; Accepted 6 December 2017; Published 7 February 2018

Academic Editor: Tongliang Liu

Copyright © 2018 Li Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The sparse representation based classification (SRC) method and collaborative representation based classification (CRC) method have attracted more and more attention in recent years due to their promising results and robustness. However, both SRC and CRC algorithms directly use the training samples as the dictionary, which leads to a large fitting error. In this paper, we propose the Laplace graph embedding class specific dictionary learning (LGECSDL) algorithm, which trains a weight matrix and embeds a Laplace graph to reconstruct the dictionary. Firstly, it can increase the dimension of the dictionary matrix, which can be used to classify the small sample database. Secondly, it gives different dictionary atoms with different weights to improve classification accuracy. Additionally, in each class dictionary training process, the LGECSDL algorithm introduces the Laplace graph embedding method to the objective function in order to keep the local structure of each class, and the proposed method is capable of improving the performance of face recognition according to the class specific dictionary learning and Laplace graph embedding regularizer. Moreover, we also extend the proposed method to an arbitrary kernel space. Extensive experimental results on several face recognition benchmark databases demonstrate the superior performance of our proposed algorithm.