• DocumentCode
    1798334
  • Title

    Incremental face recognition using rehearsal and recall processes

  • Author

    Sangwook Kim ; Mallipeddi, R. ; Minho Lee

  • Author_Institution
    Sch. of Electron. Eng., Kyungpook Nat. Univ., Taegu, South Korea
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    2752
  • Lastpage
    2757
  • Abstract
    Most of the machine learning algorithms particularly suffer from the plasticity-stability dilemma. In this paper, we propose a model that adopts two types of memories i.e. short-term memory (STM) and long-term memory (LTM), which share their information through control processes called rehearsal and recall to alleviate the dilemma. In addition, the proposed model tries to integrate the advantages of generative and discriminative classifiers by employing them in STM and LTM respectively. Experimental results show the importance of rehearsal and recall process in improving the performance of the algorithm.
  • Keywords
    face recognition; image classification; learning (artificial intelligence); LTM learning process; STM learning process; discriminative classifiers; generative classifiers; incremental face recognition; long-term memory; recall process; rehearsal process; short-term memory; Data models; Face recognition; Feature extraction; Principal component analysis; Robustness; Support vector machines; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), 2014 International Joint Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6627-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2014.6889902
  • Filename
    6889902