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Robust facial expression recognition system in higher poses.

Ebenezer Owusu1, Justice Kwame Appati2, Percy Okae3

  • 1Department of Computer Science, University of Ghana, P. O. Box LG 163, Accra, Ghana.

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Summary

This study enhances facial expression recognition (FER) accuracy in severe head poses using a novel 3D head-tracking algorithm. The advanced method significantly improves performance across multiple facial expression databases.

Keywords:
Ada-AdaSVMEllipsoidal modelFacial expressionsGabor filtersThree-dimensional head pose

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Area of Science:

  • Computer Vision
  • Biometrics
  • Human-Computer Interaction

Background:

  • Facial expression recognition (FER) is crucial for applications in security, neuroscience, and psychology.
  • Current FER systems struggle with accuracy in severe head poses, limiting their real-world applicability.
  • Addressing this challenge is vital for advancing FER technology.

Purpose of the Study:

  • To improve the prediction accuracy of facial expression recognition (FER) specifically in cases of severe head poses.
  • To introduce a robust 3D head-tracking algorithm integrating an ellipsoidal model, ensemble AdaBoost, and saturated vector machine (SVM).

Main Methods:

  • Utilized an ellipsoidal tracking model for robust feature tracking across video frames.
  • Employed Gabor filters for the extraction of visible expressive facial key points.
  • Implemented an advanced ensemble algorithm (Ada-AdaSVM) for effective feature selection and classification.

Main Results:

  • The proposed technique demonstrated outstanding overall performance across five diverse facial expression databases (Bosphorus, BU-3DFE, MMI, CK+, BP4D-Spontaneous).
  • Significant improvements in recognition accuracy were observed, particularly under challenging severe head pose conditions.

Conclusions:

  • The developed 3D head-tracking algorithm effectively overcomes the limitations of severe head poses in FER.
  • This robust approach offers a promising solution for enhancing the reliability and accuracy of facial expression recognition systems.