Sparsity Preserving-Based Local Fisher Discriminant Analysis with Applications in Face Recognition

Author

Li, Changbin

Author_Institution

Coll. of Math. & Comput. Sci., Qinzhou Coll., Qinzhou, China

fYear

2012

fDate

17-19 Aug. 2012

Firstpage

460

Lastpage

463

Abstract

A kind of algorithm called sparsity preserving-based local fisher discriminant analysis (SPLFDA) is proposed, which insulates sparsity preserving projections and local fisher discriminant analysis in the process of dimensionality reduction. It inherits the special character of geometrical structure preserving and neighborhood preserving. Experiments operated on UMIST, Yale and YaleB face dataset show that the algorithm is more effective.

Keywords

face recognition; statistical analysis; visual databases; SPLFDA; UMIST dataset; Yale dataset; YaleB face dataset; dimensionality reduction; face recognition; geometrical structure preservation; neighborhood preservation; sparsity preserving projections; sparsity preserving-based local Fisher discriminant analysis; Accuracy; Algorithm design and analysis; Classification algorithms; Databases; Face; Face recognition; Training; face recognition; local fisher discriminant analysis; semi-supervised dimensionality reduction; sparsity preserving;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on

Conference_Location

Chongqing

Print_ISBN

978-1-4673-2406-9

Type

conf

DOI

10.1109/ICCIS.2012.289

Filename

6300002

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3312188