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How much training data for facial action unit detection?

Jeffrey M Girard1, Jeffrey F Cohn2, László A Jeni3

  • 1Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.

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PubMed
Summary
This summary is machine-generated.

For facial action unit (AU) detection, using more subjects in training data is more effective than more frames per subject. Appearance-based methods benefit most from increased subject numbers for efficient performance.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Biomedical Signal Processing

Background:

  • Facial action unit (AU) detection is crucial for understanding human emotions and expressions.
  • Optimizing training data size is essential for improving the accuracy and efficiency of AU detection models.

Purpose of the Study:

  • To investigate how training set size, specifically the number of subjects and frames per subject, impacts appearance-based and shape-based facial AU detection.
  • To determine the most efficient data sampling strategy for training AU detection classifiers.

Main Methods:

  • Utilized digital video data from 80 subjects (over 350,000 frames) with expert-coded facial activity.
  • Trained and tested support vector machine classifiers using shape-normalized SIFT descriptors (appearance features) and 66 facial landmarks (shape features).
  • Employed ten-fold cross-validation to systematically vary the number of subjects and frames per subject.

Main Results:

  • Appearance-based classifiers showed incremental performance improvement with increased subjects (8 to 64), irrespective of frames per subject (450-3600).
  • Shape-based classifiers exhibited mixed results with varying numbers of subjects and frames.
  • Maximal performance for appearance features was achieved with a large number of subjects and as few as 450 frames per subject.

Conclusions:

  • The number of subjects in the training set is a more critical factor than the number of frames per subject for optimizing facial AU detection performance.
  • Appearance-based features are more robust to variations in training data size, particularly benefiting from increased subject diversity.
  • Findings suggest prioritizing subject diversity over data volume per subject for efficient and effective facial AU detection model training.