Topological principal component analysis for face encoding and recognition

被引：9
|
作者：
Pujol, A
Vitrià, J
Lumbreras, F
Villanueva, JJ
机构：
[1] Univ Autonoma Barcelona, Comp Vis Lab, Bellaterra 08193, Cerdanyola Vall, Spain
[2] Univ Autonoma Barcelona, Dept Informat, Bellaterra 08193, Cerdanyola Vall, Spain
来源：
PATTERN RECOGNITION LETTERS | 2001年 / 22卷 / 6-7期
关键词：
generalization; principal component analysis; face recognition; topological covariance matrix; covariance estimation;
D O I：
10.1016/S0167-8655(01)00027-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Principal component analysis (PCA)-like methods make use of an estimation of the covariances between sample variables. This estimation does not take into account their topological relationships. This paper proposes how to use these relationships in order to estimate the covariances in a more robust way. The new method topological principal component analysis (TPCA) is tested using both face encoding and recognition experiments showing how the generalization capabilities of PCA are improved. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：769 / 776
页数：8
相关论文
共 50 条
  • [1] Diagonal principal component analysis for face recognition
    Zhang, DQ
    Zhou, ZH
    Chen, SC
    [J]. PATTERN RECOGNITION, 2006, 39 (01) : 140 - 142
  • [2] Principal component net analysis for face recognition
    He, Lianghua
    Hu, Die
    Jiang, Changjun
    [J]. MICAI 2006: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 4293 : 734 - +
  • [3] Face Recognition Using Principal Component Analysis
    Kaur, Ramandeep
    Himanshi, Er.
    [J]. 2015 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC), 2015, : 585 - 589
  • [4] Face Recognition for Criminal Identification: An implementation of principal component analysis for face recognition
    Abdullah, Nurul Azma
    Saidi, Md. Jamri
    Ab Rahman, Nurul Hidayah
    Wen, Chuah Chai
    Hamid, Isredza Rahmi A.
    [J]. 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST'17), 2017, 1891
  • [5] Robust Principal Component Analysis for Sparse Face Recognition
    Wang, Ling
    Cheng, Hong
    [J]. PROCEEDINGS OF THE 2013 FOURTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP), 2013, : 171 - 176
  • [6] ROBUST ADAPTED PRINCIPAL COMPONENT ANALYSIS FOR FACE RECOGNITION
    Chen, Shaokang
    Lovell, Brian C.
    Shan, Ting
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2009, 23 (03) : 491 - 520
  • [7] Face recognition with weighted kernel principal component analysis
    Liu, Nan
    Wang, Han
    Yau, Wei-Yun
    [J]. 2006 9TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION, VOLS 1- 5, 2006, : 445 - +
  • [8] Face Recognition using Euler Principal Component Analysis
    Boon, Yinn Xi
    Ch'ng, Sue Inn
    [J]. 2015 4TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION ICIEV 15, 2015,
  • [9] Modular Image Principal Component Analysis for Face Recognition
    Pereira, Jose Francisco
    Cavalcanti, George D. C.
    Ren, Tsang Ing
    [J]. IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, 2009, : 1969 - 1974
  • [10] Face recognition using kernel principal component analysis
    Kim, KI
    Jung, K
    Kim, HJ
    [J]. IEEE SIGNAL PROCESSING LETTERS, 2002, 9 (02) : 40 - 42
12345