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Enhanced facial expression recognition using 3D point sets and geometric deep learning.

Duc-Phong Nguyen1, Marie-Christine Ho Ba Tho1, Tien-Tuan Dao2

  • 1Université de technologie de Compiègne, CNRS, Biomechanics and Bioengineering, Centre de Recherche Royallieu, CS 60 319 - 60 203, Compiègne Cedex, France.

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|May 24, 2021
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Summary
This summary is machine-generated.

This study introduces geometric deep learning for 3D facial expression recognition using point cloud data. The novel approach achieves promising accuracy, advancing human-computer interaction and aiding in rehabilitation for facial palsy patients.

Keywords:
3D point cloudFacial expression recognitionGeometric deep learningHuman facePointNet++

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Area of Science:

  • Computer Vision and Machine Learning
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Traditional facial expression recognition relies on 2D image processing, demanding complex feature engineering and conventional machine learning.
  • These 2D methods are often sensitive to variations and may not capture the full complexity of facial dynamics.
  • A more robust approach is needed to enhance accuracy and applicability in real-world scenarios.

Purpose of the Study:

  • To develop and evaluate a novel facial expression recognition system utilizing geometric deep learning on 3D point cloud data.
  • To explore the direct processing of 3D facial scans for improved expression recognition accuracy.
  • To establish a foundation for applying advanced AI in clinical settings, such as guiding rehabilitation for facial palsy patients.

Main Methods:

  • Utilized two 3D facial expression databases: Bosphorus (65 subjects, 7 expressions) and SIAT-3DFE (150 subjects, 4 expressions).
  • Applied preprocessing techniques including face center cropping, data augmentation, and point cloud denoising.
  • Implemented and optimized a geometric deep learning model, PointNet++, through hyperparameter tuning.

Main Results:

  • Achieved 69.01% accuracy for 7 basic expressions and 85.85% for 5 specific expressions on the Bosphorus database.
  • Obtained a recognition rate of 78.70% on the SIAT-3DFE database.
  • Demonstrated the efficacy of direct 3D point cloud processing for facial expression recognition.

Conclusions:

  • Geometric deep learning applied directly to 3D point cloud data offers a powerful new method for facial expression recognition.
  • The developed model shows significant potential for enhancing human-computer interaction and understanding nuanced emotional states.
  • Future applications include personalized rehabilitation programs for patients with facial palsy, optimizing their recovery process.