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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Quantifying Facial Gestures Using Deep Learning in a New World Monkey.

Filippo Carugati1, Dayanna Curagi Gorio1, Chiara De Gregorio1,2

  • 1Department of Life Sciences and Systems Biology, Università di Torino, Torino, Italy.

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Summary

Deep learning advances automated analysis of primate facial gestures. Markerless pose estimation accurately distinguishes cotton-top tamarin facial expressions in different contexts, improving communication studies.

Keywords:
DeepLabCutSaguinus oedipuscotton‐top tamarinmarkerless pose estimationprimate face

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Area of Science:

  • Primate Ethology
  • Computational Biology
  • Animal Behavior

Background:

  • Facial gestures are key in primate multimodal communication.
  • Current methods for facial data extraction are manual, subjective, and time-consuming.
  • Automated tools, particularly deep learning, offer potential for objective analysis.

Purpose of the Study:

  • To explore the distinctiveness of facial gestures in cotton-top tamarins using automated methods.
  • To develop and validate a deep learning model for recognizing specific facial landmarks.
  • To assess the model's ability to classify facial configurations associated with vocalizations and behaviors.

Main Methods:

  • Utilized markerless pose estimation algorithms on video footage of captive cotton-top tamarins.
  • Developed a custom model by manually labeling facial landmarks.
  • Trained the model to predict landmark positions and generate distance matrices.
  • Employed machine learning classifiers to discriminate facial configurations.

Main Results:

  • Achieved correct classification rates exceeding 80% for distinguishing voiced from unvoiced facial configurations.
  • Demonstrated context-specific facial gesture recognition, with high accuracy during yawning, social activity, and resting.
  • Validated the potential of automated analysis for primate facial communication.

Conclusions:

  • Markerless pose estimation shows significant promise for advancing primate multimodal communication research.
  • Automated facial gesture analysis can effectively distinguish between different behavioral contexts in cotton-top tamarins.
  • This approach represents a critical step towards automated behavioral cue extraction from video data.