Collaborative Representation Based Graph Embedding Discriminant Analysis for Face Recognition

被引：0

作者：

Du J. ^{[1
]}

Zheng L. ^{[1
]}

Shi J. ^{[2
]}

机构：

[1] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei

[2] School of Software, Hefei University of Technology, Hefei

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2022年 / 34卷 / 06期

关键词：

Collaborative representation; Face recognition; Graph embedding; Label propagation; Semi-supervised learning;

D O I：

10.3724/SP.J.1089.2022.19009

中图分类号：

学科分类号：

摘要：

Considering the collaborative representation projection constructs the adjacent graph based on the col-laborative representation relationship of samples and many samples from different classes may be gathered to-gether after projection, a discriminative competitive and collaborative representation projection is proposed. Firstly, each sample is competitively and collaboratively represented by all samples in the dataset to calculate the similarity of samples. Then, an intraclass graph is constructed to characterize the intraclass compactness, and an interclass graph is built to characterize the interclass separability. On this basis, the label propagation algorithm is applied to calculate the soft label information of unlabeled samples to eliminate the influence of unlabeled sam-ples on the recognition results. Additionally, the nonlinear mapping is used to replace the linear inner in the graph embedding framework to solve the linearly inseparable problem of the original samples in the low-dimensional space. Experimental results on ORL, AR, FERET and Yale B face datasets show that compared to the CRP me-thod, the proposed method improves the maximum recognition rate by 1%~4% and improves 2%~6% in noise and blur results, respectively. © 2022, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：878 / 891

页数：13