Face recognition using discriminant locality preserving projections based on maximum margin criterion

被引：118

作者：

Lu, Gui-Fu ^{[1
,2
]}

Lin, Zhong ^{[1
]}

Jin, Zhong ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China

[2] Anhui Univ Technol & Sci, Dept Comp Sci & Engn, Wuhu 241000, Anhui, Peoples R China

来源：

PATTERN RECOGNITION | 2010年 / 43卷 / 10期

关键词：

MMC; Locality preserving; Small sample size problem; Feature extraction; Face recognition; DIMENSIONALITY REDUCTION;

D O I：

10.1016/j.patcog.2010.04.007

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, we propose a new discriminant locality preserving projections based on maximum margin criterion (DLPP/MMC). DLPP/MMC seeks to maximize the difference, rather than the ratio, between the locality preserving between-class scatter and locality preserving within-class scatter. DLPP/MMC is theoretically elegant and can derive its discriminant vectors from both the range of the locality preserving between-class scatter and the range space of locality preserving within-class scatter. DLPP/MMC can also derive its discriminant vectors from the null space of locality preserving within-class scatter when the parameter of DLPP/MMC approaches + infinity. Experiments on the ORL, Yale, FERET, and PIE face databases show the effectiveness of the proposed DLPP/MMC. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：3572 / 3579

页数：8

共 25 条

[1]

[Anonymous], 1973, Pattern Classification and Scene Analysis

[2]

BELHUMEUR V, 1997, ANAL MACHINE INTELLI, V19, P711

[3] Laplacian eigenmaps for dimensionality reduction and data representation [J].

Belkin, M ;

Niyogi, P .

NEURAL COMPUTATION, 2003, 15 (06) :1373-1396

[4] A new LDA-based face recognition system which can solve the small sample size problem [J].

Chen, LF ;

Liao, HYM ;

Ko, MT ;

Lin, JC ;

Yu, GJ .

PATTERN RECOGNITION, 2000, 33 (10) :1713-1726

[5] An efficient discriminant-based solution for small sample size problem [J].

Das, Koel ;

Nenadic, Zoran .

PATTERN RECOGNITION, 2009, 42 (05) :857-866

[6] REGULARIZED DISCRIMINANT-ANALYSIS [J].

FRIEDMAN, JH .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1989, 84 (405) :165-175

[7]

Fukunaga K, 1990, INTRO STAT PATTERN R, V2nd

[8] Face recognition using Laplacianfaces [J].

He, XF ;

Yan, SC ;

Hu, YX ;

Niyogi, P ;

Zhang, HJ .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2005, 27 (03) :328-340

[9] Learning a locality preserving subspace for visual recognition [J].

He, XF ;

Yan, SC ;

Hu, YX ;

Zhang, HJ .

NINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS I AND II, PROCEEDINGS, 2003, :385-392

[10] Orthogonal neighborhood preserving discriminant analysis for face recognition [J].

Hu, Haifeng .

PATTERN RECOGNITION, 2008, 41 (06) :2045-2054

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