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Selective Transfer Machine for Personalized Facial Action Unit Detection.

Wen-Sheng Chu1, Fernando De la Torre1, Jeffery F Cohn2

  • 1Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|September 23, 2014
PubMed
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This study introduces a Selective Transfer Machine (STM) to improve automatic facial action unit (AFA) detection by personalizing generic classifiers without new labels. STM effectively reduces person-specific biases, enhancing facial expression analysis accuracy.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automatic facial action unit (AFA) detection is crucial for facial expression analysis.
  • Existing methods often overlook individual differences in facial morphology and behavior, limiting classifier generalization.
  • Training person-specific classifiers is often impractical.

Purpose of the Study:

  • To propose an unsupervised method for personalizing generic classifiers for AFA detection.
  • To address the challenge of individual differences in facial expression analysis.
  • To improve the generalization of AFA detection models to unseen individuals.

Main Methods:

  • Introduced a transductive learning method called Selective Transfer Machine (STM).
  • STM learns a personalized classifier by re-weighting training samples relevant to the test subject.

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  • This approach attenuates person-specific biases without requiring additional labels for test subjects.

    • Main Results:

    • STM demonstrated superior performance compared to generic classifiers across three major databases (CK+, GEMEP-FERA, RU-FACS).
    • The method effectively personalized generic classifiers, outperforming baseline approaches.
    • STM showed significant improvements in AFA detection accuracy by accounting for individual variations.

    Conclusions:

    • The proposed Selective Transfer Machine (STM) offers an effective unsupervised approach to personalize facial action unit detection models.
    • STM successfully mitigates the impact of individual differences, enhancing classifier performance on unseen subjects.
    • This method provides a viable alternative to person-specific training for robust facial expression analysis.