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A Gabor-block-based kernel discriminative common vector approach using cosine kernels for human face recognition.

Arindam Kar1, Debotosh Bhattacharjee, Dipak Kumar Basu

  • 1Indian Statistical Institute, Kolkata 700108, India. kgparindamkar@gmail.com

Computational Intelligence and Neuroscience
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new face recognition method using nonlinear Gabor Wavelet Transform (GWT) and Kernel Discriminative Common Vector (KDCV) with cosine kernels. The approach effectively extracts discriminant features from low-energy image blocks for improved recognition accuracy.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Face recognition systems require robust feature extraction methods.
  • Traditional methods may struggle with variations in pose and illumination.
  • Gabor Wavelet Transform (GWT) is effective for texture analysis but requires further discriminant feature extraction.

Purpose of the Study:

  • To propose a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition.
  • To improve the accuracy and robustness of face recognition systems.
  • To integrate GWT with a generalized Kernel Discriminative Common Vector (KDCV) method.

Main Methods:

  • Extraction of low-energized blocks from Gabor wavelet transformed images.
  • Application of a generalized Kernel Discriminative Common Vector (KDCV) method with cosine kernel functions for nonlinear feature extraction.
  • Utilizing only kernel discriminative eigenvectors associated with nonzero eigenvalues for positive kernel discriminative vectors.

Main Results:

  • The proposed method successfully extracts discriminant features from low-energized blocks.
  • The approach demonstrates effectiveness in classifying both frontal and pose-angled faces.
  • Experimental results on FRAV2D and FERET databases validate the method's performance.

Conclusions:

  • The nonlinear Gabor-block-based generalized KDCV method with cosine kernels is a feasible and effective approach for face recognition.
  • This method enhances face recognition by extracting discriminative features robustly.
  • The approach shows significant promise for real-world face recognition applications.