PCA plus F-LDA: A new approach to face recognition

被引：4
|
作者：
Wang, Huiyuan [1 ]
Wang, Zengfeng [2 ]
Leng, Yan [3 ]
Wu, Xiaojuan [4 ]
Li, Qing [5 ]
机构：
[1] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Shandong, Peoples R China
[2] Ludong Univ, Sch Comp Sci & Technol, Yantai 264025, Shandong, Peoples R China
[3] Shandong Normal Univ, Coll Phys & Elect, Jinan 250014, Shandong, Peoples R China
[4] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Shandong, Peoples R China
[5] Shandong Meteorol Bur, Jinan 250031, Shandong, Peoples R China
来源：
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2007年 / 21卷 / 06期
基金：
中国国家自然科学基金;
关键词：
face recognition; feature extraction; subspace analysis; linear discriminant analysis; fractional-step linear discriminant analysis;
D O I：
10.1142/S021800140700579X
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new feature extraction method for face recognition based on principal component analysis (PCA) and fractional-step linear discriminant analysis (F-LDA) is given in this paper. In order to reduce the computation complexity, PCA is first used to reduce the dimension. In addition, before using F-LDA, we transform the pooled within-class scatter matrix into an identity matrix. The proposed method is tested on AR and UMIST face databases. Experiment results show that our method gains higher classification accuracy than other existing methods used in the experiment.
引用
收藏
页码：1059 / 1068
页数：10
相关论文
共 50 条
  • [1] Resampling LDA/QR and PCA plus LDA for face recognition
    Liu, J
    Chen, SC
    [J]. AI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2005, 3809 : 1221 - 1224
  • [2] Comparative Research of PCA plus AdaBoost and PCA plus LDA for Face Recognition Technology
    Fan, ShaSha
    Du, Yang
    Zhao, Jia
    Wang, Qiang
    Guo, Fei
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL INDUSTRIAL INFORMATICS AND COMPUTER ENGINEERING CONFERENCE, 2015, : 1009 - 1013
  • [3] Face Recognition using Fusion of PCA and LDA: Borda Count Approach
    Borade, Sushma Niket
    Deshmukh, Ratnadeep R.
    Ramu, Sivakumar
    [J]. 2016 24TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED), 2016, : 1164 - 1167
  • [4] An Extended PCA and LDA for color face recognition
    Kang, Qiong
    Peng, Lingling
    [J]. THIRD INTERNATIONAL CONFERENCE ON INFORMATION SECURITY AND INTELLIGENT CONTROL (ISIC 2012), 2012, : 345 - 348
  • [5] Image PCA: A new approach for face recognition
    Wen, Ying
    Shi, Pengfei
    [J]. 2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL I, PTS 1-3, PROCEEDINGS, 2007, : 1241 - 1244
  • [6] Face recognition based on PCA image reconstruction and LDA
    Zhou, Changjun
    Wang, Lan
    Zhang, Qiang
    Wei, Xiaopeng
    [J]. OPTIK, 2013, 124 (22): : 5599 - 5603
  • [7] Support vector machines, PCA and LDA in face recognition
    Mazanec, Jan
    Melisek, Martin
    Oravec, Milos
    Pavlovicova, Jarmila
    [J]. JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS, 2008, 59 (04): : 203 - 209
  • [8] Line-based PCA and LDA approaches for face recognition
    Nhat, VDM
    Lee, S
    [J]. ADVANCES IN NATURAL COMPUTATION, PT 2, PROCEEDINGS, 2005, 3611 : 101 - 104
  • [9] Face recognition research based on improved PCA and improved LDA
    Gan, Junying
    Shao, Pan
    Xiao, Juan
    [J]. Journal of Information and Computational Science, 2010, 7 (12): : 2513 - 2520
  • [10] Performance Analysis of Combination of CS with PCA and LDA for Face Recognition
    Preeti
    Kumar, Dinesh
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION, INSTRUMENTATION AND CONTROL (ICICIC), 2017,
12345