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Adaptively weighted sub-directional two-dimensional linear discriminant analysis for face recognition

Authors:	Lijun YanAuthor Vitae Shu-Chuan Chu^{Author Vitae}

Affiliation:	^a Department of Automatic of Test and Control, Harbin Institute of Technology, 92 West Da-Zhi Street, Harbin, Heilongjiang, 150001, China ^b Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus of Shenzhen University Town, Xili, Shenzhen, 518055, China ^c Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Chien Kung Campus, 415 Chien Kung Road, Kaohsiung, 807, Taiwan ^d School of Computer Science, Engineering and Mathematics, Flinders University of South Australia, GPO Box 2100, Adelaide, South Australia 5001, Australia ^e Center of Excellence in Information Assurance, King Saud University, P.O. Box 92144, Riyadh, Saudi Arabia

Abstract:	A novel image classification algorithm named Adaptively Weighted Sub-directional Two-Dimensional Linear Discriminant Analysis (AWS2DLDA) is proposed in this paper. AWS2DLDA can extract the directional features of images in the frequency domain, and it is applied to face recognition. Some experiments are conducted to demonstrate the effectiveness of the proposed method. Experimental results confirm that the recognition rate of the proposed system is higher than the other popular algorithms.

Keywords:	Face recognition Directional filter banks Two-dimensional linear discriminant analysis
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