A framework for face recognition using Laplacian eigenmaps and nearest feature mixtures

CHUR > College Computer Science and Informatics > Department of Computer Science and Information Engineering > Seminar Papers > Item 987654321/32321

Please use this identifier to cite or link to this item: http://chur.chu.edu.tw/handle/987654321/32321

Title:	A framework for face recognition using Laplacian eigenmaps and nearest feature mixtures
Authors:	李建興 Lee, Chang-Hsing
Contributors:	資訊工程學系 Computer Science & Information Engineering
Keywords:	Face recognition;covariance matrix;nearest feature point;nearest feature line;Fisher criterion
Date:	2010
Issue Date:	2014-06-27 01:51:01 (UTC+8)
Abstract:	Many researchers exert to find the best discriminant transformation in eigenspaces to reduce the facial pose, illumination, and expression (PIE) impacts for obtaining the better recognition results. Covariance matrix which represents dimensional correlati
Appears in Collections:	[Department of Computer Science and Information Engineering] Seminar Papers

Files in This Item:

File	Description	Size	Format
s_e331_0500.pdf		29Kb	Adobe PDF	31	View/Open