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Abstract: Linear discriminant analysis (LDA) is popular feature extraction technique for face recognition. However, it often suffers the small sample size problem when dealing with the high dimensional face data. Moreover, the within-class and between-class scatter matrix used in LDA have low effective when dealing with face data of non-gaussian density. In this paper, we propose a new method for face recognition. We first calculate the weighted dual personal subspaces to replace the within and between... (Update)
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BibTeX entry: (Update)
@misc{ qiu-null,
author = "Recognition Xipeng Qiu",
title = "Null Space-Based Lda With Weighted Dual Personal Subspaces For Face",
url = "citeseer.ist.psu.edu/748125.html" }
Citations (may not include all citations):897 Introduction to statistical pattern recognition (context) - Fukunaga - 1990
495 Eigenfaces for recognition (context) - Turk, Pentland - 1991
124 Eigenfaces vs. Fisherfaces: Recognition using class specific.. - Belhumeur, Hespanda et al. - 1997
90 The feret database and evaluation procedure for face recogni.. (context) - Phillips, Wechsler et al. - 1998
77 Face recognition: A literature survey - Zhao, Chellappa et al. - 2003
41 Parameterisation of a stochastic model for human face identi.. - Samaria, Harter - 1994
16 Bayesian face recognition (context) - Moghaddam, Jebara et al. - 2000
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