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Emotion Recognition from Facial Expressions Considering Individual Differences in Emotional Intelligence.

Yubin Kim1, Ayoung Cho1, Hyunwoo Lee1

  • 1Department of Emotion Engineering, Sangmyung University, Seoul 03016, Republic of Korea.

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Summary

Emotional intelligence (EI) stratified training data improves facial expression recognition (FER) performance, especially in ambiguous naturalistic settings. This data-centric approach enhances affective data consistency for better emotion recognition models.

Keywords:
affective ambiguitydata consistencydata-centric emotion recognitionemotional intelligencefacial expression recognition

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Area of Science:

  • Psychology
  • Computer Science
  • Affective Computing

Background:

  • Facial Expression Recognition (FER) in naturalistic settings faces challenges due to label ambiguity and inconsistent stimulus-response alignment.
  • A data-centric approach is crucial for improving FER by considering qualitative factors in training data.

Purpose of the Study:

  • To investigate the impact of emotional intelligence (EI)-stratified training data on FER performance.
  • To treat EI as a factor influencing affective data consistency and its effect on FER.

Main Methods:

  • Collected naturally elicited facial expressions in a controlled experiment with arousal and valence ratings.
  • Grouped participants into High and Low EI based on subjective evaluations and affect estimator outputs.
  • Trained binary classifiers for arousal and valence recognition using EI-stratified data and evaluated performance across different test sets.

Main Results:

  • EI-stratified training showed context-dependent performance differences, particularly in baseline and ambiguous conditions.
  • No significant performance differences were observed under unambiguous conditions.
  • Item-level analyses revealed significant classification correctness differences in specific task-condition combinations.

Conclusions:

  • FER performance is influenced by both model architecture and the statistical coherence of training data.
  • EI-informed data selection can enhance FER in ambiguity-prone naturalistic scenarios.
  • Findings support the importance of data quality and structure in developing robust emotion recognition systems.