NDLI: Image cross-covariance analysis for face recognition

Content Provider	IEEE Xplore Digital Library
Author	Sanguansat, P. Asdornwised, W. Marukata, S. Jitapunkul, S.
Copyright Year	2007
Description	Author affiliation: Chulalongkorn Univ., Bangkok (Sanguansat, P.; Asdornwised, W.; Jitapunkul, S.)
Abstract	In this paper, we proposed a novel technique for face recognition using image cross-covariance analysis (ICCA), based on the two-dimensional principal component analysis (2DPCA) technique. In conventional 2DPCA, the image covariance matrix is directly calculated via 2D images in matrix form, by concept of the covariance of a random variable. We found that it is not the optimal solution for 2DPCA framework. Because some useful information for classification is neglected. Thus, we introduced an image cross-covariance matrix which is a generalized form of the image covariance matrix. This matrix is defined by two variables. The first variable is the original image and the second one is the shifted version of the former. In this way, all information can be analyzed by 2DPCA frameworks. In this paper, the singular value decomposition (SVD) of image cross-covariance matrix is used to determine the optimal projection matrices. Experimental results on Yale, ORL and AR face databases show the improvement of our proposed techniques over the conventional 2DPCA technique.
Starting Page	1
Ending Page	4
File Size	224656
Page Count	4
File Format	PDF
ISBN	9781424412716
DOI	10.1109/TENCON.2007.4428819
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-30
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image analysis Face recognition Covariance matrix Matrix decomposition Principal component analysis Image databases Singular value decomposition Variable speed drives Random variables Information analysis
Content Type	Text
Resource Type	Article

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