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Summary

This study introduces a new pipeline to quantify mouth shape variations and link them to perceived emotions in facial expressions. The method uses statistical shape analysis and machine learning for better emotion recognition from subtle mouth movements.

Keywords:
dimension reductionnonparametric regressionpenalized regressionshape analysissmile perception

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Area of Science:

  • Computer Vision
  • Psychology
  • Biomedical Engineering

Background:

  • Facial expressions are key to emotion recognition.
  • Quantifying subtle mouth shape variations remains challenging.
  • Existing methods lack generalizability for emotion perception.

Purpose of the Study:

  • To develop a robust pipeline for quantifying mouth shape variation.
  • To correlate mouth shape features with perceived emotions from facial expressions.
  • To enhance understanding of how mouth shape influences emotion perception.

Main Methods:

  • Utilized open-source data of 802 raters evaluating 27 smile-like expressions.
  • Employed statistical shape analysis with 30 landmarks to parameterize mouth shapes.
  • Developed a nonparametric multinomial regression model for high-dimensional data.

Main Results:

  • A 3D representation of landmark coordinates yielded superior predictive performance.
  • The developed model produced interpretable predictions linking mouth shape to emotion.
  • Demonstrated a quantifiable relationship between mouth shape and perceived emotions.

Conclusions:

  • The proposed analysis pipeline effectively quantifies mouth shape variation.
  • Subtle mouth shape changes significantly impact perceived facial emotions.
  • This method offers enhanced understanding and interpretability in facial expression analysis.