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.
Abstract (translated)
面部表情识别（FER）在面对文化差异和感知退化图像条件下必须保持其鲁棒性，然而大多数现有的评估方法假设数据具有同质性和高质量的图像。我们介绍了一个基于代理的、流式的基准测试，揭示了跨文化的组成与逐步模糊如何相互作用来影响面部表情识别的鲁棒性。 每个代理在冻结的CLIP特征空间中操作，并且使用轻量级残差适配器在线训练（sigma=0），然后在整个测试过程中保持固定。这些代理在一个5x5的晶格上移动和交互，而环境则以有计划的高斯模糊方式提供输入，随着sigma值的变化逐步增加图像模糊度。 我们研究了单文化群体（仅西方、仅亚洲）以及平衡混合（5/5）和不平衡混合（8/2, 2/8）组成的多文化环境中的人群。此外，还探讨了不同的空间接触结构对结果的影响。 实验结果显示，在不同文化群体间存在明显的非对称退化曲线：JAFFE（亚洲人群）在低模糊度下保持较高的性能，但在中等模糊阶段性能急剧下降；而KDEF（西方人群）则表现得更加均匀地随模糊程度增加而逐渐降低。混合人群中表现出中间模式，平衡的混合理论上可以减轻早期退化的负面影响，但不平衡设置会放大高模糊条件下的多数群体弱点。 这些发现量化了文化组成和交互结构在感知条件恶化的背景下对FER鲁棒性的影响。
URL
https://arxiv.org/abs/2510.13557
