Citation

Yo, Ming Chun and Chong, Siew Chin and Wee, Kuok Kwee and Chong, Lee Ying (2022) Sparse Representation with Principal Component Analysis in Face Recognition. Journal of System and Management Sciences, 12 (5). pp. 57-72. ISSN 1816-6075, 1818-0523

Text Vol.12.No.05.04.pdf - Published Version Restricted to Repository staff only Download (386kB)

Abstract

Face recognition has been one of the most reliable biometric technologies due to its easy and non-intrusive method during acquisition procedure. Multiple algorithms and methods have been developed and invented by the researchers and computer scientists in order to increase and improve the performance of face recognition. Sparse representation method has attracted a lot of attention in the fields of machine learning recently and it boosts the research of sparsity-based pattern recognition among the researchers. In this research paper, we aim to investigate the impact of sparse representation in face recognition. The proposed method utilizes the fusion of Principal Component Analysis and Sparse Representation Classification to enhance the accuracy of the face recognition. Experimental results demonstrate that the proposed method can achieved almost 99% accuracy using the FERET dataset.

Item Type:	Article
Uncontrolled Keywords:	sparse representation, face recognition, FERET dataset, classification, feature extraction.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	05 Dec 2022 01:17
Last Modified:	05 Dec 2022 01:17
URII:	http://shdl.mmu.edu.my/id/eprint/10882

Downloads

Edit (login required)