Feature selection for face recognition based on data partitioning

被引:0
|
作者
Singh, S [1 ]
Singh, M [1 ]
Markou, M [1 ]
机构
[1] Univ Exeter, Dept Comp Sci, Exeter EX4 4PT, Devon, England
来源
16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL I, PROCEEDINGS | 2002年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Feature selection is an important consideration in several applications where one needs to choose a smaller subset of features from a complete set of raw measurements such that the improved subset generates as good or better classification performance compared to original data. In this paper, we describe a novel feature selection approach that is based on the estimation of classification complexity though data partitioning. This approach allows us to select the N best features from a given set in order of their ability to separate data from different classes. In this paper, we perform our experiments on the ORL face database that consists of 400 images. The results show that the proposed approach outperforms the probability distance approach and is a viable method for implementing more advanced search methods of feature selection.
引用
收藏
页码:680 / 683
页数:4
相关论文
共 50 条
  • [1] Face recognition based on feature selection strategy
    Tang, Hengliang
    Sun, Yanfeng
    Zhu, Jie
    Zhao, Mingru
    Journal of Information and Computational Science, 2014, 11 (12): : 4203 - 4210
  • [2] A face recognition algorithm based on optimal feature selection
    Zhao K.
    Wang D.
    Wang Y.
    Revue d'Intelligence Artificielle, 2019, 33 (02) : 105 - 109
  • [3] BEST VIEW SELECTION WITH GEOMETRIC FEATURE BASED FACE RECOGNITION
    Deboeverie, Francis
    Veelaert, Peter
    Philips, Wilfried
    2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), 2012, : 1461 - 1464
  • [4] Feature selection based on KPCA, SVM and GSFS for face recognition
    Li, WH
    Gong, WG
    Liang, YX
    Chen, WM
    PATTERN RECOGNITION AND IMAGE ANALYSIS, PT 2, PROCEEDINGS, 2005, 3687 : 344 - 350
  • [5] SSGA & EDA Based Feature Selection and Weighting for Face Recognition
    Abegaz, Tamirat
    Dozier, Gerry
    Bryant, Kelvin
    Adams, Joshua
    Shelton, Joseph
    Ricanek, Karl
    Woodard, Damon L.
    2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2011, : 1375 - 1381
  • [6] Face feature selection and recognition based on different types of Margin
    Li, Wei-Hong
    Chen, Wei-Min
    Yang, Li-Ping
    Gong, Wei-Guo
    Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2007, 29 (07): : 1744 - 1748
  • [7] Particle Swarm Optimization Based Feature Selection for Face Recognition
    Eleyan, Alaa
    2019 SEVENTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION PROCESSING AND COMMUNICATIONS (ICDIPC 2019), 2019, : 1 - 4
  • [8] Face Recognition Based on Grey Wolf Optimization for Feature Selection
    Saabia, Abd AL-BastRashed
    El-Hafeez, TarekAbd
    Zaki, Alaa M.
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT SYSTEMS AND INFORMATICS 2018, 2019, 845 : 273 - 283
  • [9] Optimal feature selection based on mutual information for face recognition
    Tang, Xu-Sheng
    Ou, Zong-Ying
    Su, Tie-Ming
    Hu, Qing-Ni
    Hua, Shun-Gang
    Dalian Ligong Daxue Xuebao/Journal of Dalian University of Technology, 2008, 48 (01): : 84 - 89
  • [10] Unsupervised Spectral Feature Selection for Face Recognition
    Zhang, Zhihong
    Hancock, Edwin R.
    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 1787 - 1790