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
收藏
页码：229 / 238
页数：10
相关论文
共 50 条
  • [41] Principal Component Analysis Integrating Mahalanobis Distance for Face Recognition
    Fan, Zizhu
    Ni, Ming
    Sheng, Meibo
    Wu, Zejiu
    Xu, Baogen
    2013 SECOND INTERNATIONAL CONFERENCE ON ROBOT, VISION AND SIGNAL PROCESSING (RVSP), 2013, : 89 - 92
  • [42] Patch-Based Principal Component Analysis for Face Recognition
    Jiang, Tai-Xiang
    Huang, Ting-Zhu
    Zhao, Xi-Le
    Ma, Tian-Hui
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2017, 2017
  • [43] Further Research on Principal Component Analysis Method of Face Recognition
    Meng, Hao
    Ke, Xiaohua
    2008 INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION: (ICMA), VOLS 1 AND 2, 2008, : 420 - 424
  • [44] Applications of adaptive principal components extraction algorithm in the feature extraction of human face
    Gan, JY
    Zhang, YW
    Mao, SY
    PROCEEDINGS OF 2001 INTERNATIONAL SYMPOSIUM ON INTELLIGENT MULTIMEDIA, VIDEO AND SPEECH PROCESSING, 2001, : 332 - 335
  • [45] Multilinear principal component analysis for face recognition with fewer features
    Wang, Jin
    Barreto, Armando
    Wang, Lu
    Chen, Yu
    Rishe, Naphtali
    Andrian, Jean
    Adjouadi, Malek
    NEUROCOMPUTING, 2010, 73 (10-12) : 1550 - 1555
  • [46] Pattern recognition by means of linear discriminant analysis and the principal components analysis
    Mokeev A.V.
    Mokeev V.V.
    Pattern Recognition and Image Analysis, 2015, 25 (04) : 685 - 691
  • [47] Selecting the principal feature components in the three-dimensional parameter space for face recognition
    Li Junbao
    Chu Shuchuan
    Pan Jengshyang
    ICEMI 2007: PROCEEDINGS OF 2007 8TH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOL I, 2007, : 27 - 31
  • [48] Automatic Signature Recognition And Verification Using Principal Components Analysis
    Ismail, I. A.
    Ramadan, M. A.
    El Danf, T.
    Samak, A. H.
    COMPUTER GRAPHICS, IMAGING AND VISUALISATION - MODERN TECHNIQUES AND APPLICATIONS, PROCEEDINGS, 2008, : 356 - +
  • [49] Speech Recognition Using Principal Components Analysis and Neural Networks
    Shabani, Shaham
    Norouzi, Yaser
    2016 IEEE 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS), 2016, : 90 - 95
  • [50] Face Recognition at a Distance: Scenario Analysis and Applications
    Vera-Rodriguez, R.
    Fierrez, J.
    Tome, P.
    Ortega-Garcia, J.
    DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 2010, 79 : 341 - 348
12345