Graph-Optimized Line Discriminant Analysis for Face Recognition

Author

Wentie Wu ; Yingchun Lu ; Xuelin Chen

Author_Institution

Sch. of Math. & Comput. Sci., Mianyang Normal Univ., Mianyang, China

Volume

2

fYear

2012

fDate

28-29 Oct. 2012

Firstpage

50

Lastpage

53

Abstract

A Graph-optimized Linear Discriminant Analysis (GLDA) for face recognition is proposed, which redefine the intrinsic and penalty graph and trade off the importance degrees of the same-class points to the intrinsic graph and the importance degrees of the not-same-class points to the penalty graph by a strictly monotone decreasing function. Experiments on Yale, YaleB, UMIST face dataset are provided for demonstrating our results.

Keywords

face recognition; graph theory; GLDA; UMIST face dataset; YaleB face dataset; face recognition; graph-optimized line discriminant analysis; importance degrees; intrinsic graph; not-same-class points; penalty graph; strictly monotone decreasing function; Algorithm design and analysis; Classification algorithms; Face; Face recognition; Linear discriminant analysis; Principal component analysis; dimensionality reduction; face recognition; fisher discriminant analysis; sparsity preserving;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-2646-9

Type

conf

DOI

10.1109/ISCID.2012.164

Filename

6405563