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已经EI收录 恭喜恭喜 Check record to add to Selected Records Add to selected records 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 Full-text and Local Holdings Links |
2楼2015-08-22 16:09:11
fatewu
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Accession number: 20150700511111 Article in Press Information about Article in Press Title: Dual-kernel based 2D linear discriminant analysis for face recognition Authors: Liu, Xiao-Zhang1 Email author liuxiaozhang@gmail.com; Ye, Hong-Wei2 Author affiliation: 1 School of Computer Science, Dongguan University of Technology, Dongguan, China 2 School of Electronics and Information Engineering, Heyuan Polytechnic, Heyuan, China Corresponding author: Liu, Xiao-Zhang Source title: Journal of Ambient Intelligence and Humanized Computing Abbreviated source title: J. Ambient Intell. Humanized Comput. Issue date: April 23, 2014 Publication year: 2014 Language: English ISSN: 18685137 E-ISSN: 18685145 Document type: Article in Press Publisher: Springer Verlag 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 Page count: 6 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 DOI: 10.1007/s12652-014-0230-2 Database: Compendex Compilation and indexing terms, © 2015 Elsevier Inc. |
3楼2015-08-22 16:09:47