Face Recognition Using Nearest Feature Space Embedding

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题名:	Face Recognition Using Nearest Feature Space Embedding
作者:	Chen,YN;Han,CC;Wang,CT;Fan,KC
贡献者:	資訊工程學系
关键词:	NONPARAMETRIC DISCRIMINANT-ANALYSIS;NONLINEAR DIMENSIONALITY REDUCTION;FEATURE LINE METHOD;CLASSIFICATION;PROJECTION;FRAMEWORK;PCA;LAPLACIANFACES;RETRIEVAL;MATRIX
日期:	2011
上传时间:	2012-03-27 18:57:54 (UTC+8)
出版者:	國立中央大學
摘要:	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.
關聯:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
显示于类别:	[資訊工程學系] 期刊論文

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