Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    A lyapunov theory-based neural network approach for face recognition

    Access Status
    Fulltext not available
    Authors
    Ang, L.
    Lim, Hann
    Seng, K.
    Chin, S.
    Date
    2009
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Ang, L. and Lim, H. and Seng, K. and Chin, S. 2009. A lyapunov theory-based neural network approach for face recognition. In Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, 23-48.
    Source Title
    Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications
    DOI
    10.4018/978-1-60566-798-0.ch002
    ISBN
    9781605667980
    School
    Curtin Sarawak
    URI
    http://hdl.handle.net/20.500.11937/2710
    Collection
    • Curtin Research Publications
    Abstract

    This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher's Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems. © 2010, IGI Global.

    Related items

    Showing items related by title, author, creator and subject.

    • Lyapunov theory-based multilayered neural network
      Lim, Hann; Seng, K.; Ang, L.; Chin, S. (2009)
      This brief presents a Lyapunov theory-based weight adaptation scheme for a multilayered neural network (MLNN) mainly used to classify a multiple-input-multiple-output (MIMO) problem. Initially, the MLNN system is linearized ...
    • MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition
      Lim, King Hann; Seng, Kah Phooi; Ang, Li-Minn (2012)
      Lyapunov theory-based radial basis function neural network (RBFNN) is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO) classification. Multidimensional input is ...
    • Intra color-shape classification for traffic sign recognition
      Lim, Hann; Seng, K.; Ang, L. (2010)
      This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.