DocumentCode :
3459222
Title :
A Research for Face Recognition Based on Locally Linear Embedding Algorithm
Author :
Gan, Junying ; Shao, Pan ; Yu, Yibin
Author_Institution :
Sch. of Inf. Eng., Wuyi Univ., Jiangmen, China
fYear :
2010
fDate :
21-23 Oct. 2010
Firstpage :
1
Lastpage :
4
Abstract :
In face recognition, traditional linear dimensionality reduction methods can not be good at keeping the intrinsic distribution of face sample data. While Locally Linear Embedding (LLE) algorithm, which belongs to manifold learning, has the advantage of keeping the intrinsic distribution of face sample data. Principal Component Analysis (PCA) possesses the merits of high recognition efficiency. An improved PCA, in which the formula of PCA is modified, is presented in this paper. This algorithm has the ability of gray normalization and can overcome the influence of light on the target. Then the algorithm is combined with LLE and used in face recognition. In this way, we not only keep the intrinsic distribution of face sample data, but also assure the accuracy of the image characteristics. Experimental results on ORL face database demonstrate that the algorithm is superior to the original LLE.
Keywords :
data reduction; face recognition; learning (artificial intelligence); principal component analysis; ORL face database; face recognition; face sample data; gray normalization; image characteristics; intrinsic distribution; linear dimensionality reduction; linear embedding algorithm; manifold learning; principal component analysis; Algorithm design and analysis; Eigenvalues and eigenfunctions; Face; Face recognition; Manifolds; Principal component analysis; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (CCPR), 2010 Chinese Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-7209-3
Electronic_ISBN :
978-1-4244-7210-9
Type :
conf
DOI :
10.1109/CCPR.2010.5659306
Filename :
5659306
Link To Document :
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