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Face recognition by applying wavelet subband representation and kernel associative memory.

Bai-Ling Zhang1, Haihong Zhang, Shuzhi Sam Ge

  • 1School of Information Technology, Bond University, Gold Coast, QLD 4229, Australia. bzhang@csm.vu.edu.au

IEEE Transactions on Neural Networks
|September 25, 2004
PubMed
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This study introduces an efficient face recognition method using 2-D wavelet subband coefficients and kernel associative memory. The approach achieves superior accuracy on multiple datasets by effectively capturing facial features with low computational cost.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Traditional face recognition methods often struggle with limited sample sizes and computational complexity.
  • Principal Component Analysis (PCA) and low-resolution image representations have limitations in capturing detailed facial features.

Purpose of the Study:

  • To propose an efficient face recognition scheme utilizing 2-D wavelet subband coefficients and kernel associative memory.
  • To enhance face recognition accuracy and efficiency, especially with limited training data.

Main Methods:

  • Representing face images using two-dimensional (2-D) wavelet subband coefficients for efficient feature extraction.
  • Employing a modular, personalized classification method based on kernel associative memory (AM) models.

Related Experiment Videos

  • Improving AM model performance by applying kernel transforms and mapping high-dimensional feature spaces back to the input space.

    • Main Results:

    • Wavelet subband coefficients efficiently capture substantial facial features with low computational complexity compared to PCA.
    • The proposed kernel associative memory approach significantly improves recognition accuracy.
    • Extensive experiments on FERET, XM2VTS, and ORL datasets demonstrate superior performance over existing methods.

    Conclusions:

    • The proposed face recognition scheme offers enhanced accuracy and efficiency.
    • The combination of 2-D wavelet features and kernel associative memory is effective for face recognition, particularly with limited data.
    • This method provides a robust solution for real-world face recognition challenges.