解决微表情识别中局部线索被稀释的问题，提升分类准确率。
SRE-FER: Regional residual evidence learning for mitigating local evidence dilution in fine-grained facial expression recognition
- 引入区域残差证据学习框架，保留细微肌肉信号
- 在 RAF-DB 等三个数据集上达到最高 92.76% 准确率
- 无需外部人脸检测，适合实际应用部署
细粒度面部表情识别依赖于区分相邻情绪的细微肌肉线索。然而，现有方法面临困境：基于检测器的方法依赖脆弱的关键点流程；而直接使用 DINOv3 等基础模型进行全局读出时，会因早期全局聚合导致稀疏肌肉信号被稀释，造成恐惧/惊讶、悲伤/中性等类别间持续混淆。为此，提出 SRE-FER 框架，其核心模块 RERA 通过零初始化残差逻辑，在不改变骨干网络全局预测的前提下，精修类别边界。训练阶段利用 FACS 解剖先验引导区域特征聚焦表达相关区域，无需推理时依赖外部人脸管道。可选的 Full 设置进一步路由样本特定非冗余令牌。在 RAF-DB、FERPlus、AffectNet-7 三个基准上分别取得 92.76%、91.32%、67.78% 的准确率，性能优于现有方法。
原文摘要 · Abstract (English)
Fine-grained facial expression recognition (FER) hinges on capturing subtle muscular cues that distinguish adjacent emotions. Yet capturing these cues presents a dilemma. Detector-based methods depend on fragile landmark pipelines, whereas we find that directly transferring foundation models such as DINOv3 under conventional global readouts can cause local evidence dilution: early global aggregation washes out sparse muscular signals and leaves persistent confusion between categories such as fear/surprise and sad/neutral. To recover this evidence, we propose SRE-FER, a readout-level regional residual evidence learning framework. Its core module, RERA, adds zero-initialized residual logits that refine class boundaries while preserving the backbone's global prediction. Training-time action unit (AU) guidance steers regional features toward expression-relevant areas using Facial Action Coding System (FACS)-based anatomical priors, without requiring an external facial pipeline at inference. An optional Full setting further routes sample-specific non-redundant tokens. On three benchmarks, SRE-FER attains 92.76% on RAF-DB, 91.32% on FERPlus, and 67.78% on AffectNet-7, demonstrating highly competitive performance compared to existing FER methods.
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