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Title: Dual collaborative representation based discriminant projection for face recognition
Authors: Huang, Pu
Shen, Yangyang
Yang, Zhangjing
Zhang, Chuanyi
Yang, Guowei
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Huang, P., Shen, Y., Yang, Z., Zhang, C. & Yang, G. (2022). Dual collaborative representation based discriminant projection for face recognition. Computers and Electrical Engineering, 102, 108281-.
Journal: Computers and Electrical Engineering
Abstract: Collaborative representation based techniques have shown promising results for face recognition; however, most of them code the samples by taking the overall samples as a dictionary, which may contain much noise information. To tackle this problem, a new face recognition algorithm, namely dual collaborative representation based discriminant projection (DCRDP), is proposed in this paper. In DCRDP, each training sample is reconstructed via dual collaborative representation to enhance the robustness to noise information: the first collaborative representation is used to choose an appropriate dictionary with respect to the training sample, while the second collaborative representation is used to find collaborative representation relationships between samples. After dual collaborative representation, DCRDP constructs two adjacency graphs to model the similarity and dissimilarity between samples, and then finds the optimal projection matrix for dimension reduction. Experiments on ExtYaleB, AR and CMU PIE face datasets verify the superiority of DCRDP to some other state-of-the-art approaches.
ISSN: 0045-7906
DOI: 10.1016/j.compeleceng.2022.108281
Schools: School of Computer Science and Engineering 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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