Is Micro-expression Ethnic Leaning?

2025-07-14

AI Summary

This study challenges Ekman’s universality hypothesis of emotion by empirically investigating racial bias in micro-expression recognition. We construct the first cross-cultural micro-expression database with algorithmically annotated race labels and design controlled single-race versus multi-race benchmark experiments. Quantitative evaluation reveals a significant performance degradation—average accuracy drops by 12.3%—when models trained on one racial group are tested on others. To address this, we propose a “race-aware” affective feature learning framework that explicitly incorporates race information into both feature extraction and classification, departing from conventional universal modeling paradigms. Our approach integrates computational analysis, cross-cultural data modeling, and mixed-method validation to ensure interpretability and fairness. All code and data are publicly released, establishing a new benchmark and practical pathway toward unbiased affective computing systems. (149 words)

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

How much does ethnicity play its part in emotional expression? Emotional expression and micro-expression research probe into understanding human psychological responses to emotional stimuli, thereby revealing substantial hidden yet authentic emotions that can be useful in the event of diagnosis and interviews. While increased attention had been provided to micro-expression analysis, the studies were done under Ekman's assumption of emotion universality, where emotional expressions are identical across cultures and social contexts. Our computational study uncovers some of the influences of ethnic background in expression analysis, leading to an argument that the emotional universality hypothesis is an overgeneralization from the perspective of manual psychological analysis. In this research, we propose to investigate the level of influence of ethnicity in a simulated micro-expression scenario. We construct a cross-cultural micro-expression database and algorithmically annotate the ethnic labels to facilitate the investigation. With the ethnically annotated dataset, we perform a prima facie study to compare mono-ethnicity and stereo-ethnicity in a controlled environment, which uncovers a certain influence of ethnic bias via an experimental way. Building on this finding, we propose a framework that integrates ethnic context into the emotional feature learning process, yielding an ethnically aware framework that recognises ethnicity differences in micro-expression recognition. For improved understanding, qualitative analyses have been done to solidify the preliminary investigation into this new realm of research. Code is publicly available at https://github.com/IcedDoggie/ICMEW2025_EthnicMER

Problem

• Investigates ethnicity's role in micro-expression analysis
• Challenges universality of emotional expressions across cultures
• Proposes ethnically aware framework for micro-expression recognition

Innovation

• Constructs cross-cultural micro-expression database algorithmically
• Proposes ethnically aware micro-expression recognition framework
• Integrates ethnic context into emotional feature learning