Title: Facial feature extraction based on principal component analysis and class independent kernel sparse representation
Authors: Xin Xiong; Li Kefeng
Addresses: School of Electrical Information Engineering, Henan University of Engineering, 451191, Zhengzhou Henan, China ' School of Electrical Information Engineering, Henan University of Engineering, 451191, Zhengzhou Henan, China
Abstract: In this paper, classification algorithm of kernel sparse representation has been proposed based on robust principal component analysis by using RPCA technology to generate redundant dictionary and kernel sparse representation to structure classifier and has been used for face recognition. Firstly, each training sample matrix has been decomposed into a low rank matrix and a sparse error matrix by using RPCA technology, so as to structure base dictionary and error dictionary by using the low rank matrix and error matrix respectively and generate redundancy dictionary of sparse representation of test samples. Then, kernel regularised orthogonal matching pursuit (KROMP) algorithm has been proposed to get sparse representation coefficient which has been used to complete classification and recognition of test samples. Compared with similar algorithms, algorithm in the thesis is of a high recognition rate for face recognition and has a strong ability to adapt to noise and error interference.
Keywords: principal component analysis; PCA; image recognition; sparse representation; face recognition; facial feature; feature extraction.
International Journal of Reasoning-based Intelligent Systems, 2018 Vol.10 No.2, pp.149 - 154
Available online: 24 May 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article