scholarly journals Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness

10.5772/6638 ◽  
2009 ◽  
Author(s):  
Wangmeng Zuo ◽  
Kuanquan Wang ◽  
Hongzhi Zhang
Keyword(s):  
Face Recognition ◽  
Subspace Methods

Subspace methods for face recognition

2010 ◽  
Vol 4 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Ashok Rao ◽  
S. Noushath
Keyword(s):  
Face Recognition ◽  
Subspace Methods

NON-NEGATIVE MATRIX FACTORIZATION FRAMEWORK FOR FACE RECOGNITION

2005 ◽  
Vol 19 (04) ◽  
pp. 495-511 ◽  
Author(s):  
YUAN WANG ◽  
YUNDE JIA ◽  
CHANGBO HU ◽  
MATTHEW TURK
Keyword(s):  
Face Recognition ◽  
Matrix Factorization ◽  
Recognition Performance ◽  
Image Representation ◽  
Subspace Methods ◽  
Intuitive Notion ◽  
Orthogonal Bases ◽  
Representation Method ◽  
Non Negative Matrix Factorization

Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results. Based on the framework, we present two novel subspace methods: Fisher Non-negative Matrix Factorization (FNMF) and PCA Non-negative Matrix Factorization (PNMF). FNMF adds both the non-negative constraint and the Fisher constraint to matrix factorization. The Fisher constraint maximizes the between-class scatter and minimizes the within-class scatter of face samples. Subsequently, FNMF improves the capability of face recognition. PNMF adds the non-negative constraint and characteristics of PCA, such as maximizing the variance of output coordinates, orthogonal bases, etc. to matrix factorization. Therefore, we can get intuitive features and desirable PCA characteristics. Our experiments show that FNMF and PNMF achieve better face recognition performance than NMF and Local NMF.

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