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Related Experiment Videos

Kernel machine-based one-parameter regularized fisher discriminant method for face recognition.

Wen-Sheng Chen1, Pong C Yuen, Jian Huang

  • 1Department of Mathematics, Shenzhen University, Shenzhen 518060, China. chenws@szu.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 1, 2005
PubMed
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This study introduces a new Kernel machine-based One-Parameter Regularized Fisher Discriminant (K1PRFD) for face recognition. K1PRFD effectively handles pose, illumination variations, and the small sample size problem, outperforming existing methods.

Area of Science:

  • Computer Science
  • Machine Learning
  • Pattern Recognition

Background:

  • Linear Discriminant Analysis (LDA) struggles with pose and illumination variations in face recognition.
  • The small sample size (S3) problem causes singularity in LDA's within-class scatter matrix.
  • Nonlinear distributions of face images challenge traditional linear methods.

Purpose of the Study:

  • To address limitations of traditional LDA in face recognition.
  • To propose a novel Kernel machine-based One-Parameter Regularized Fisher Discriminant (K1PRFD) technique.
  • To develop an optimal parameter selection method for K1PRFD.

Main Methods:

  • Developed K1PRFD by integrating a one-parameter regularized discriminant analysis with kernel methods.
  • Utilized the conjugate gradient method for simultaneous optimization of kernel and regularization parameters.

Related Experiment Videos

  • Evaluated K1PRFD on FERET, Yale Group B, and CMU PIE face databases.

    • Main Results:

    • K1PRFD demonstrates superior performance compared to existing LDA-based methods.
    • The proposed method effectively handles pose and illumination variations.
    • The technique successfully overcomes the small sample size problem in face recognition.

    Conclusions:

    • K1PRFD offers a robust solution for face recognition challenges.
    • The proposed parameter optimization method enhances K1PRFD's effectiveness.
    • This approach shows significant potential for improving automated face recognition systems.