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Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image.

Xudong Xie1, Kin-Man Lam

  • 1Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong. enxdxie@eie.polyu.edu.hk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 5, 2006
PubMed
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This study introduces a novel doubly nonlinear mapping kernel PCA for enhanced human face recognition. The method effectively extracts and transforms facial features, improving recognition accuracy across multiple databases.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Kernel PCA (KPCA) nonlinearly maps data to high-dimensional spaces for linear separability.
  • Conventional KPCA often overlooks the structural characteristics of facial images.
  • Determining effective nonlinear mappings for face recognition remains challenging.

Purpose of the Study:

  • To propose a novel Gabor-based kernel PCA with doubly nonlinear mapping for human face recognition.
  • To enhance feature extraction and transformation by incorporating facial structure.
  • To improve the discriminating power and spatial adaptivity of facial features.

Main Methods:

  • Utilizing Gabor wavelets for facial feature extraction.
  • Introducing a doubly nonlinear mapping kernel PCA (DKPCA).

Related Experiment Videos

  • Developing a new nonlinear mapping in the original feature space using an eigenmask to emphasize important facial points.

    • Main Results:

    • The proposed DKPCA method demonstrated improved feature discrimination and spatial adaptivity.
    • Consistent and promising results were achieved across the Yale, AR, ORL, and YaleB databases.
    • The algorithm outperformed standard PCA and Gabor wavelets plus PCA/KPCA methods.

    Conclusions:

    • The novel Gabor-based DKPCA offers a more effective approach to human face recognition.
    • Emphasizing important facial features through eigenmasks enhances recognition performance.
    • The method provides a robust and adaptable solution for face recognition tasks.