NDLI: Kernel subspace LDA with convolution kernel function for face recognition

Content Provider	IEEE Xplore Digital Library
Author	Wen-Sheng Chen Yuen, P.C. Zhen Ji
Copyright Year	2010
Description	Author affiliation: Department of computer Science, Hong Kong Baptist University, Hong Kong (Yuen, P.C.) \|\| College of Computer Science and Software Engineering, Shenzhen University, 518060, China (Zhen Ji) \|\| College of Mathematics and Computational Science, Shenzhen University, 518060, China (Wen-Sheng Chen)
Abstract	It is well-known that most wavelet functions are un-symmetrical and thus fail to satisfy Fourier criterion. These kinds of wavelets cannot be utilized to construct Mercer kernel directly. Based on convolution technique, this paper proposes a novel framework on Mercer kernel construction. The proposed methodology indicates that any of wavelets can generate a wavelet-like kernel basis function, which has zero vanishing moment. An example on convolution Mercer kernel construction is given by using Haar wavelet. The self-constructed Haar wavelet convolution kernel (HWCK) function is then applied to kernel subspace linear discriminant analysis (SLDA) approach for face classification. The CMU PIE human face dataset is selected for evaluation. Comparing with the RBF kernel based SLDA method and existing LDA-based kernel methods such as KDDA and GDA, the proposed Haar wavelet convolution kernel based method gives superior results.
Starting Page	158
Ending Page	163
File Size	847879
Page Count	6
File Format	PDF
ISBN	9781424465309
e-ISBN	9781424465316
DOI	10.1109/ICWAPR.2010.5576309
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear Discriminant Analysis Mercer Kernel Face Recognition
Content Type	Text
Resource Type	Article

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