Face Recognition Based on Joint Sparse Representation of Multiple Features for Public Safety

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Wei, Li^1 ; Yongbin, Zhao^1 ; Jieping, Han^2 ; Zhiru, Zhang^1 ; Hai, Yu^1

State Grid Liaoning Electric Power Supply Co. Ltd, Information and Telecommunication Branch, Shenyang

110006, China^1

Northeast Electric Power University, Changchun

132012, China^2

关键词: Face recognition methods;    Feature vectors;    Multiple features;    Nonnegative matrix factorization;    Principle component analysis;    Reconstruction error;    Sparse representation;    Yale B database;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/4/042018/pdf
DOI  :  10.1088/1757-899X/563/4/042018

来源: IOP

【 摘 要 】

A face recognition method based on joint sparse representation of multiple features is proposed in this paper. First, principle component analysis (PCA), kernel PCA (KPCA), and non-negative matrix factorization (NMF) are used to extract feature vectors of face images. The three features could provide complementary descriptions for face images. Then, in the classification stage, joint sparse representation is employed to classify the three features thus considering their correlations. Finally, the total reconstruction errors of the three features on different kinds of training classes are calculated to determine the label of test sample. Experiments are conducted on AR and Yale-B databases to validate the effectiveness of the proposed method.