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【答案】应助回帖
★ ★ ★ ★ ★ ★ ★ ★ ★ ★
感谢参与，应助指数 +1
aboyhw: 金币+5, ★★★★★最佳答案 2015-08-22 16:32:56
aboyhw: 金币+5, ★★★★★最佳答案 2015-08-22 16:34:47
心静_依然: LS-EPI+1, 感谢应助 2015-08-22 16:51:49
已经EI收录 恭喜恭喜
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Dual-kernel based 2D linear discriminant analysis for face recognition
Liu, Xiao-Zhang1 Email author liuxiaozhang@gmail.com; Ye, Hong-Wei2
Source: Journal of Ambient Intelligence and Humanized Computing, April 23, 2014; ISSN: 18685137, E-ISSN: 18685145; DOI: 10.1007/s12652-014-0230-2; Publisher: Springer Verlag
Article in Press Information about Article in Press
Author affiliations:
1 School of Computer Science, Dongguan University of Technology, Dongguan, China
2 School of Electronics and Information Engineering, Heyuan Polytechnic, Heyuan, China
Abstract:
This paper proposes a new image feature extraction method for face recognition, called dual-kernel based two dimensional linear discriminant analysis (D-K2DLDA), by integrating multiple kernel discriminant analysis with the existing K2DFDA method. The proposed method deals with a face image directly as a matrix, instead of a stacked vector from rows or columns of the image. Moreover, we separately perform an iterative scheme for kernel parameter optimization for each of the two kernels, based on the maximum margin criterion and the damped Newton’s method, followed by a fusion procedure of the two kernels. Experimental results on the ORL and UMIST face databases show the effectiveness of D-K2DLDA. © 2014 Springer-Verlag Berlin Heidelberg
Main heading: Face recognition
Controlled terms: Discriminant analysis - Feature extraction - Iterative methods - Matrix algebra
Uncontrolled terms: 2D linear discriminant analysis - Image feature extractions - Iterative schemes - Kernel parameter optimization - Linear discriminant analysis - Matrix representation - Maximum margin criterions - Multiple kernels
Classification Code: 716 Telecommunication; Radar, Radio and Television - 921.1 Algebra - 921.6 Numerical Methods - 922 Statistical Methods
Database: Compendex
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