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Related Experiment Videos

A multiple maximum scatter difference discriminant criterion for facial feature extraction.

Fengxi Song1, David Zhang, Dayong Mei

  • 1New Star Research Institute of Applied Technology, Hefei 230031, China. songfengxi@yahoo.com

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 9, 2008
PubMed
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The new multiple maximum scatter difference (MMSD) method enhances facial feature extraction by generalizing a binary criterion. MMSD offers superior performance over existing methods for large-scale facial recognition tasks.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • The Maximum Scatter Difference (MSD) criterion is a binary discriminant criterion for pattern classification.
  • MSD addresses the singularity problem in small-sample-size problems by using generalized scatter difference.
  • Existing MSD classifiers are effective for face recognition but inefficient for large-scale tasks due to their binary nature.

Purpose of the Study:

  • To generalize the binary MSD criterion to a multiple counterpart for improved facial feature extraction.
  • To introduce the Multiple Maximum Scatter Difference (MMSD) discriminant criterion and its associated feature-extraction method.
  • To enhance the efficiency and performance of facial recognition systems on large-scale datasets.

Main Methods:

Related Experiment Videos

  • The study proposes the Multiple Maximum Scatter Difference (MMSD) discriminant criterion.
  • MMSD is a novel subspace-based feature-extraction method.
  • It computes discriminant vectors from the range of the between-class scatter matrix and the null space of the within-class scatter matrix.

Main Results:

  • The MMSD method demonstrates superior performance compared to state-of-the-art facial feature-extraction techniques.
  • Experiments conducted on the FERET benchmark database validate the effectiveness of MMSD.
  • MMSD outperforms methods including null space method, direct Linear Discriminant Analysis (LDA), Eigenface, Fisherface, and Complete LDA.

Conclusions:

  • The MMSD discriminant criterion provides an effective solution for large-scale facial feature extraction.
  • The MMSD method is theoretically sound, computationally efficient, and outperforms existing approaches.
  • This generalization significantly advances the field of facial recognition and pattern classification.