Face Recognition Using Nearest Feature Space Embedding

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/51628

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51628

Title:	Face Recognition Using Nearest Feature Space Embedding
Authors:	Chen,YN;Han,CC;Wang,CT;Fan,KC
Contributors:	資訊工程學系
Keywords:	NONPARAMETRIC DISCRIMINANT-ANALYSIS;NONLINEAR DIMENSIONALITY REDUCTION;FEATURE LINE METHOD;CLASSIFICATION;PROJECTION;FRAMEWORK;PCA;LAPLACIANFACES;RETRIEVAL;MATRIX
Date:	2011
Issue Date:	2012-03-27 18:57:54 (UTC+8)
Publisher:	國立中央大學
Abstract:	Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce the impacts, many researchers have been trying to find the best discriminant transformation in eigenspaces, either linear or nonlinear, to obtain better recognition results. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space embedding (called NFS embedding) algorithm is proposed for face recognition. The distance between a point and the nearest feature line (NFL) or the NFS is embedded in the transformation through the discriminant analysis. Three factors, including class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces. The proposed method was evaluated by several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.
Relation:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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