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Jeffrey M Girard1, Jeffrey F Cohn2, Fernando De la Torre3

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Estimating facial expression intensity requires direct ground truth data. Using binary classifier decision values as a shortcut for intensity is unreliable for accurate facial expression analysis.

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Area of Science:

  • Computer Vision
  • Affective Computing
  • Human-Computer Interaction

Background:

  • Facial expressions convey crucial information, with both occurrence and intensity being vital.
  • Automatic detection of facial expression occurrence has advanced, but intensity estimation remains challenging.
  • Collecting intensity ground truth is resource-intensive, leading to exploration of alternative methods.

Purpose of the Study:

  • To evaluate the efficacy of using decision values from binary-trained classifiers as a proxy for facial expression intensity.
  • To determine the optimal approach for accurate facial expression intensity estimation, particularly for smiles.
  • To demonstrate the performance of intensity-trained models compared to heuristic methods.

Main Methods:

  • Empirical evaluation of decision values from binary-trained maximum margin classifiers.
  • Training and comparison of multiclass and regression models using direct intensity ground truth.
  • Testing across multiple databases with varied feature extraction and dimensionality reduction techniques.

Main Results:

  • The heuristic of using binary classifier decision values for intensity estimation was found to be flawed theoretically and practically.
  • Intensity-trained multiclass and regression models significantly outperformed binary-trained classifier decision values in smile intensity estimation.
  • Multiclass models demonstrated superior performance even in detecting smile occurrence compared to binary-trained classifiers.

Conclusions:

  • Accurate facial expression intensity estimation, especially for smiles, necessitates the collection and use of direct intensity ground truth data.
  • Intensity-trained models offer a reliable and high-performance solution for facial expression intensity analysis.
  • Investing in ground truth data collection for intensity yields significant improvements in facial expression recognition accuracy.