Factored principal components analysis, with applications to face recognition

被引：11

作者：

Dryden, Ian L. ^{[1
]}

Bai, Li ^{[2
]}

Brignell, Christopher J. ^{[1
]}

Shen, Linlin ^{[3
]}

机构：

[1] Univ Nottingham, Sch Math Sci, Nottingham NG7 2RD, England

[2] Univ Nottingham, Sch Comp Sci, Nottingham NG7 2RD, England

[3] Shen Zhen Univ, Sch Informat & Engn, Shenzhen, Peoples R China

来源：

STATISTICS AND COMPUTING | 2009年 / 19卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

Face recognition; Forensic identification; Gabor wavelets; Kernel density estimator; Likelihood ratio; Multivariate normal; Principal components analysis; 2-DIMENSIONAL PCA; REPRESENTATION;

D O I：

10.1007/s11222-008-9087-6

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A dimension reduction technique is proposed for matrix data, with applications to face recognition from images. In particular, we propose a factored covariance model for the data under study, estimate the parameters using maximum likelihood, and then carry out eigendecompositions of the estimated covariance matrix. We call the resulting method factored principal components analysis. We also develop a method for classification using a likelihood ratio criterion, which has previously been used for evaluating the strength of forensic evidence. The methodology is illustrated with applications in face recognition.

引用

页码：229 / 238

页数：10

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