International Journal of Nonlinear Analysis and Applications

Face recognition via weighted non-negative sparse representation

Document Type : Research Paper

Authors

1 Department of Software Engineering, Sari Branch, Islamic Azad University, Sari, Iran

2 Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

3 Department of Mathematics, Iran University of Science and Technology, Tehran, Iran

Abstract

Face recognition is one of the most important tools of identification in biometrics. Face recognition has attracted great attention in the last decades and numerous algorithms have been proposed. Different researches have shown that face recognition with Sparse Representation based Classification (SRC) has great classification performance. In some applications such as face recognition, it is appropriate to limit the search space of sparse solver because of local minima problem. In this paper, we apply this limitation via two methods. In the first, we apply the nonnegative constraint of sparse coefficients. As finding the sparse representation is a problem with very local minima, at first we use a simple classifier such as nearest subspace and then add the obtained information of this classifier to the sparse representation problem with some weights. Based on this view, we propose Weighted Non-negative Sparse Representation WNNSR for the face recognition problem. A quick and effective way to identify faces based on the sparse representation (SR) is smoothed $L_0$-norm $(SL_0)$ approach. In this paper, we solve the WNNSR problem based on the $SL_0$ idea. This approach is called Weighted Non-Negative Smoothed $L_0$ norm $(WNNSL_0)$. The simulation results on the Extended Yale B database demonstrate that the proposed method has high accuracy in face recognition better than the ultramodern sparse solvers approach.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 12, Issue 2
November 2021
Pages 1141-1150
  • Receive Date: 18 February 2021
  • Revise Date: 23 April 2021
  • Accept Date: 13 May 2021