Title :
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
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2646-9
DOI :
10.1109/ISCID.2012.164
