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Facial expression recognition using three-stage support vector machines.

Issam Dagher1, Elio Dahdah2, Morshed Al Shakik2

  • 1Computer Engineering Department, University of Balamand, Tripoli, P.O.BOX 100, Lebanon. dagheri@hotmail.com.

Visual Computing for Industry, Biomedicine, and Art
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Summary

A novel three-stage support vector machine (SVM) system accurately recognizes facial expressions. This method enhances classification accuracy by using multilevel stages and histogram-oriented gradient features for robust facial expression recognition.

Keywords:
Facial expression recognitionHistogram of oriented gradientsSupport vector machineValidationViola–Jones

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition is crucial for human-computer interaction.
  • Accurate detection of subtle facial muscle movements is challenging.
  • Existing methods may struggle with complex or ambiguous expressions.

Purpose of the Study:

  • To propose a robust and accurate three-stage support vector machine (SVM) system for facial expression recognition.
  • To improve classification accuracy by employing a hierarchical SVM structure.
  • To leverage advanced feature extraction techniques for enhanced performance.

Main Methods:

  • A three-stage SVM classifier was developed, utilizing binary combinations of seven expressions.
  • Image preprocessing techniques were applied to ensure meaningful feature extraction.
  • Histogram-oriented gradient (HOG) features were employed due to their sensitivity to shape changes.
  • The system was trained and validated using leave-one-out and K-fold cross-validation methods.

Main Results:

  • The proposed three-stage SVM system demonstrated competitive performance across three diverse facial expression databases.
  • Experimental results indicated improved accuracy compared to existing facial expression recognition methods.
  • The multilevel stage approach effectively reduced classification errors for complex expressions.

Conclusions:

  • The developed three-stage SVM system offers a promising approach for accurate facial expression recognition.
  • The combination of hierarchical SVMs and HOG features provides a robust solution.
  • The system's performance suggests its potential for real-world applications in human-computer interaction.