DocumentCode
2541958
Title
Kernel Non-Locality Preserving Projection and Its Application to Face Recognition
Author
Wang, Jianguo ; Yang, Wankou ; Yan, Hui
Author_Institution
Dept. of Comput. Sci. & Technol., Tangshan Coll., Tangshan, China
fYear
2009
fDate
4-6 Nov. 2009
Firstpage
1
Lastpage
4
Abstract
Non-locality preserving projection (NLPP) is a kind of feature extraction technique based on the characterization of the non-local scatter. Due to NLPP is a linear algorithm in nature, it cannot address nonlinear problem in recognition, so a novel subspace method, called Kernel Non-locality Preserving Projection (KNLPP) discriminant analysis, is proposed for face recognition. Experimental results on two popular benchmark databases, FERET and Yale, demonstrate the effectiveness of the proposed method.
Keywords
face recognition; feature extraction; FERET; KNLPP discriminant analysis; Yale; benchmark databases; face recognition; feature extraction technique; kernel nonlocality preserving projection; linear algorithm; nonlocal scatter; subspace method; Application software; Computer science; Data mining; Face recognition; Feature extraction; Image databases; Kernel; Laplace equations; Scattering; Spatial databases;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4199-0
Type
conf
DOI
10.1109/CCPR.2009.5344046
Filename
5344046
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