Paper Info
Title
Supervised Regularization Locality-Preserving Projection Method for Face Recognition.
Abstract
Locality-preserving projection (LPP) is a promising manifold-based dimensionality reduction and linear feature extraction method for face recognition. However, there exist two main issues in traditional LPP algorithm. LPP does not utilize the class label information at the training stage and its performance will be affected for classification tasks. In addition, LPP often suffers from small sample size (3S) problem, which occurs when the dimension of input pattern space is greater than the number of training samples. Under this situation, LPP fails to work. To overcome these two limitations, this paper presents a novel supervised regularization LPP (SRLPP) approach based on a supervised graph and a new regularization strategy. It theoretically proves that regularization matrix SL R approaches to the original one as the regularized parameter tends to zero. The proposed SRLPP method is subsequently applied to face recognition. The experiments are conducted on two publicly available face databases, namely ORL database and FERET database. Compared with some existing LDA-based and LPP-based linear feature extraction approaches, experimental results show that our SRLPP approach gives superior performance. © 2012 World Scientific Publishing Company.
Year
DOI
Venue
2012
10.1142/S0219691312500531
IJWMIP
Keywords
DocType
Volume
LINEAR DISCRIMINANT-ANALYSIS,IMAGE RECOGNITION,ALGORITHM,EIGENFACES,LPP
Journal
10
Issue
ISSN
Citations 
6
0219-6913
9
PageRank 
References 
Authors
0.45
16
4
Name
Order
Citations
PageRank
Wen-Sheng Chen139139.97
Wei Wang2242.60
Jianwei Yang35812.73
Yuan Yan Tang42662209.20