Study on emotion recognition bias in different regional groups.

Area of Science:

• Computer Science
• Human-Computer Interaction
• Psychology

Background:

• Real-time human emotion recognition enhances human-machine communication.
• Facial expression recognition is hindered by lighting variations, obfuscation, and significant cultural/regional biases.
• Emotion recognition models trained on one region (e.g., North America) often fail in others (e.g., East Asia).

Purpose of the Study:

• To address the problem of regional and cultural bias in facial expression-based emotion recognition.
• To propose a novel meta-model that fuses multiple emotional cues and features for improved accuracy.
• To investigate the impact of regional specificity on emotion recognition models.

Main Methods:

• Developed a multi-cues emotion model (MCAM) integrating image features, action units, micro-expressions, and macro-expressions.
• Incorporated fine-grained content-independent features, facial muscle movements, short-term, and high-level facial expressions.
• Utilized a meta-classifier approach to fuse diverse facial attribute information.

Main Results:

• Successful classification of regional facial expressions relies on non-sympathetic features.
• Learning expressions from one regional group can negatively impact recognition of others unless trained from scratch.
• Certain facial cues and dataset features inherently prevent the design of a perfectly unbiased classifier.

Conclusions:

• To effectively learn specific regional emotional expressions, prior knowledge of other regional expressions may need to be disregarded or "forgotten".
• The proposed MCAM demonstrates a method to mitigate, but not entirely eliminate, cultural bias in emotion recognition.
• Future research should focus on developing unbiased classifiers and understanding the 'forgetting' mechanism in cross-cultural emotion recognition.