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Related Experiment Videos

Learning from examples in the small sample case: face expression recognition.

Guodong Guo1, Charles R Dyer

  • 1Computer Sciences Department, University of Wisconsin-Madison, Madison, WI 53706, USA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|June 24, 2005
PubMed
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This study introduces a new linear programming technique for face expression recognition with limited training data. The method excels in feature selection and classifier training, offering a practical solution for small sample learning challenges.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Example-based learning in computer vision is challenging with limited data.
  • Small sample learning is crucial for practical applications like face expression recognition.

Purpose of the Study:

  • To address the challenge of face expression recognition with few training images.
  • To introduce a novel linear programming technique for feature selection and classifier training.

Main Methods:

  • A pairwise linear programming framework for feature selection.
  • Development of a classifier training method using linear programming.
  • Comparison with simplified Bayes classifier, Support Vector Machine (SVM), and AdaBoost.

Main Results:

Related Experiment Videos

  • The proposed linear programming method demonstrates effectiveness in small sample face expression recognition.
  • Experimental results show competitive or superior performance compared to existing algorithms.
  • A new categorization of algorithms for small sample learning is proposed.

Conclusions:

  • Linear programming offers a robust approach for feature selection and classification in low-data scenarios.
  • The pairwise feature selection framework is particularly beneficial for small sample learning.
  • The study provides valuable insights into algorithm performance for limited data recognition tasks.