NDLI: Comparison of Kernel Class-dependence Feature Analysis (KCFA) with Kernel Discriminant Analysis (KDA) for Face Recognition

Content Provider	IEEE Xplore Digital Library
Author	Chunyan Xie Kumar, B.V.K.
Copyright Year	2007
Description	Author affiliation: Carnegie Mellon Univ., Pittsburgh (Chunyan Xie; Kumar, B.V.K.)
Abstract	Kernel methods have been applied to many linear feature analysis classifiers to generate nonlinear classifiers for improved classification performance. The recently proposed kernel class-dependence feature analysis (KCFA) method extends linear correlation filter technology to kernel correlation filters, greatly improving the classification performance. In this paper, we compare the KCFA method with the kernel discriminant analysis {KDA) method and show that the KCFA and the KDA result in the same representation subspace and the relationship between them is similar to the relationship between the orthogonal and the simplex signal representations in digital communications. We present face recognition results to illustrate that the KCFA method is preferable to the KDA method.
Starting Page	1
Ending Page	6
File Size	1729033
Page Count	6
File Format	PDF
ISBN	9781424415960
DOI	10.1109/BTAS.2007.4401947
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image analysis Face recognition Nonlinear filters Digital communication Feature extraction Performance analysis Pattern recognition Signal representations Kernel Signal analysis
Content Type	Text
Resource Type	Article

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