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The Yonsei Face Database (YFace DB) offers static and dynamic Asian facial expressions for six basic emotions. Dynamic stimuli showed higher accuracy than static ones, with notable gender effects in perception.

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Area of Science:

  • Psychology
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Facial expression databases are crucial for research in emotion recognition and affective computing.
  • Existing databases often lack diversity or include only static stimuli, limiting their ecological validity.
  • The need for a comprehensive, validated database of Asian facial expressions, encompassing both static and dynamic stimuli, is evident.

Purpose of the Study:

  • To develop and validate the Yonsei Face Database (YFace DB), a novel resource of static and dynamic facial stimuli.
  • To capture six basic emotions (happiness, sadness, anger, surprise, fear, disgust) using diverse Asian models.
  • To assess the accuracy, intensity, and naturalness of the facial expressions presented in the database.

Main Methods:

  • Compilation of static pictures and dynamic film clips from 74 Asian models (50% female, aged 19-40).
  • Validation by 221 undergraduate students rating 1,480 stimuli for accuracy, intensity, and naturalness.
  • Analysis of accuracy, intensity, and naturalness across different emotions, stimulus types (static/dynamic), and model/rater demographics.

Main Results:

  • Dynamic stimuli achieved higher overall accuracy (83%) compared to static stimuli (76%).
  • Happiness and disgust showed higher accuracy in static stimuli, while other emotions were more accurate in dynamic stimuli.
  • Significant gender effects were observed in the perception of specific emotions, with varying accuracy based on model and rater gender.

Conclusions:

  • The YFace DB is the largest and most comprehensive Asian facial expression database to date, offering both static and dynamic stimuli.
  • The validation results provide valuable insights into the reliability and perception of specific emotional expressions within the database.
  • This database serves as a significant resource for researchers studying facial emotion recognition, particularly within Asian populations.