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An Across-Target Study on Visual Attentions in Facial Expression Recognition.

Baomin Li1, Fenglei Yang2

  • 1Faculty of Education, East China Normal University, Shanghai, 200062, China.

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This study introduces a novel method for across-target analysis in human expression recognition using eye-tracking data. It reveals significant similarities in attention patterns among subjects recognizing the same facial expressions.

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Across-target analysisFacial expression recognitionVisual attention

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Area of Science:

  • Cognitive Science
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Automatic expression recognition research relies on advanced devices like eye-trackers for observing human expression processing.
  • Current studies face limitations in across-target analysis due to discrepancies in target faces, hindering the discovery of latent eye-behavior patterns.

Purpose of the Study:

  • To explore attention patterns in expression recognition by enabling across-target analysis.
  • To overcome limitations of previous studies by utilizing correspondences between different facial targets.

Main Methods:

  • Developed a method to map eye-tracking fixations from different targets onto a synthetic face, creating an across-target fixation map.
  • Tokenized fixation maps using areas of interest (AOI), analyzed in receiver operating characteristic (ROC) space, and modeled using linear regression.
  • Compared attention patterns using Pearson's correlation.

Main Results:

  • Achieved an averaged correlation values ranging from 0.60 to 0.86.
  • Demonstrated significant similarity in eye-movement patterns between subjects when recognizing identical expression classes.

Conclusions:

  • The proposed across-target analysis method effectively reveals consistent attention patterns in expression recognition.
  • Findings suggest that underlying eye-behavior patterns are shared across subjects for specific facial expressions, advancing the field of automatic expression recognition.