Modeling Cultural Bias in Facial Expression Recognition with Adaptive Agents

2025-10-15

AI Summary

Existing facial expression recognition (FER) systems exhibit insufficient robustness under cross-cultural settings and image degradation, while mainstream evaluation protocols overlook cultural diversity and variations in visual quality. Method: We propose the first adaptive-agent-based streaming benchmark framework that simulates multi-cultural group interactions within a dynamically varying Gaussian blur environment. It enables online robust evaluation via frozen CLIP features coupled with lightweight residual adapters. Results: Experiments reveal asymmetric performance degradation across cultural groups: Asian-dominant groups show strong initial advantages under low blur but suffer sharp mid-range declines, whereas Western groups degrade more uniformly. In mixed groups, culturally balanced configurations mitigate early degradation, whereas imbalance exacerbates performance collapse under high blur. This work is the first to quantitatively characterize the synergistic impact of cultural composition and perceptual degradation on FER robustness, establishing a novel evaluation paradigm and empirical foundation for fair and robust expression recognition.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Facial expression recognition (FER) must remain robust under both cultural variation and perceptually degraded visual conditions, yet most existing evaluations assume homogeneous data and high-quality imagery. We introduce an agent-based, streaming benchmark that reveals how cross-cultural composition and progressive blurring interact to shape face recognition robustness. Each agent operates in a frozen CLIP feature space with a lightweight residual adapter trained online at sigma=0 and fixed during testing. Agents move and interact on a 5x5 lattice, while the environment provides inputs with sigma-scheduled Gaussian blur. We examine monocultural populations (Western-only, Asian-only) and mixed environments with balanced (5/5) and imbalanced (8/2, 2/8) compositions, as well as different spatial contact structures. Results show clear asymmetric degradation curves between cultural groups: JAFFE (Asian) populations maintain higher performance at low blur but exhibit sharper drops at intermediate stages, whereas KDEF (Western) populations degrade more uniformly. Mixed populations exhibit intermediate patterns, with balanced mixtures mitigating early degradation, but imbalanced settings amplify majority-group weaknesses under high blur. These findings quantify how cultural composition and interaction structure influence the robustness of FER as perceptual conditions deteriorate.

Problem

• Modeling cultural bias in facial expression recognition systems
• Testing robustness under cultural variation and image degradation
• Analyzing interaction between cultural composition and blur effects

Innovation

• Agent-based streaming benchmark for cultural bias analysis
• Frozen CLIP features with lightweight residual adapters
• Cultural composition and blur interactions in lattice environments