DocumentCode :
2304563
Title :
A Novel Regularized Locality Preserving Projections for Face Recognition
Author :
Chen, Wen-Sheng ; Wang, Wei ; Yang, Jian-wei
Author_Institution :
Coll. of Math. & Comput. Sci., Shenzhen Univ., Shenzhen, China
fYear :
2011
fDate :
25-27 April 2011
Firstpage :
110
Lastpage :
113
Abstract :
Dimensionality reduction technologies are very important for pattern representation and recognition. Among them, locality preserving projection (LPP) is a manifold dimensionality reduction scheme and has been successfully applied to face recognition. However, LPP is an unsupervised linear approach, its performance will degrade for classification tasks. Especially, when the dimension of input space is greater than the number of training data, singularity problem will occur and LPP cannot be implemented directly. To tackle the draw backs of LPP algorithm, this paper proposes a novel regularized LPP(RLPP) approach using supervised graph and regularization technique. The proposed RLPP method has been tested and evaluated with two public available databases, namely ORL and FERET databases. Experimental results show that the proposed RLPP approach surpasses Laplacianface and Direct-LPP (DLPP) methods.
Keywords :
face recognition; image classification; image representation; dimensionality reduction technology; face classification task; face recognition; pattern recognition; pattern representation; regularization technique; regularized locality preserving projection; supervised graph; Accuracy; Databases; Eigenvalues and eigenfunctions; Face; Face recognition; Matrix decomposition; Training; Face recognition; Locality preserving projections; Singularity problem;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Computing (ICIC), 2011 Fourth International Conference on
Conference_Location :
Phuket Island
Print_ISBN :
978-1-61284-688-0
Type :
conf
DOI :
10.1109/ICIC.2011.26
Filename :
5954516
Link To Document :
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