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Unmasking the Devil in the Details: What Works for Deep Facial Action Coding?

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Deep learning for facial expression analysis has advanced significantly. Our study reveals key design choices, like generic pre-training, improve automated facial expression coding performance beyond current benchmarks.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Affective Computing

Background:

  • Automated facial expression coding performance is rapidly improving, driven by deep learning.
  • The impact of specific design choices on these systems remains under-explored.

Purpose of the Study:

  • To systematically evaluate critical design choices in automated facial expression coding.
  • To identify factors that contribute to state-of-the-art performance.

Main Methods:

  • Utilized the Facial Expression Recognition and Analysis (FERA 2017) database.
  • Conducted systematic evaluations of pre-training strategies, feature alignment, model size, and optimizer parameters.
  • Developed and tested a novel architecture informed by empirical findings.

Main Results:

  • Generic pre-training unexpectedly outperformed face-specific models in some scenarios.
  • Identified optimal practices for tuning optimizer details.
  • Achieved a 3.5% increase in F1 score for occurrence detection and a 5.8% increase in ICC for intensity estimation.

Conclusions:

  • Specific design choices significantly impact automated facial expression coding.
  • The developed architecture sets a new state-of-the-art on the FERA 2017 challenge.
  • Findings provide valuable insights for advancing facial expression recognition research.