Orthogonal local spline discriminant projection with application to face recognition

被引：3

作者：

Lei, Ying-Ke ^{[1
,2
,3
]}

Ding, Zhi-Guo ^{[3
]}

Hu, Rong-Xiang ^{[1
,2
]}

Zhang, Shan-Wen ^{[1
]}

Jia, Wei ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Intelligent Machines, Intelligent Comp Lab, Hefei 230031, Anhui, Peoples R China

[2] Univ Sci & Technol China, Dept Automat, Hefei 230027, Anhui, Peoples R China

[3] Inst Elect Engn, Hefei 230037, Anhui, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2011年 / 32卷 / 04期

基金：

美国国家科学基金会; 国家高技术研究发展计划(863计划);

关键词：

Feature extraction; Subspace learning; Manifold learning; Face recognition; NONLINEAR DIMENSIONALITY REDUCTION; FEATURE-EXTRACTION; NEURAL-NETWORKS; EIGENMAPS; FRAMEWORK;

D O I：

10.1016/j.patrec.2010.11.024

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, an efficient feature extraction algorithm called orthogonal local spline discriminant projection (O-LSDP) is proposed for face recognition. Derived from local spline embedding (LSE), O-LSDP not only inherits the advantages of LSE which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. Extensive experiments on several standard face databases demonstrate the effectiveness of the proposed method. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：615 / 625

页数：11

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