聚焦面部细微纹理变化，提升复杂场景下表情识别准确率
TKFNet: Learning Texture Key Factor Driven Feature for Facial Expression Recognition
- 通过识别关键纹理区域，捕捉表情细微动态特征
- 在 RAF-DB 与 KDEF 数据集上达到当前最优性能
- 适合需要高精度表情分析的智能交互系统使用
真实场景下的面部表情识别（FER）因表情特征细微且局部性强，以及面部外观复杂变化而极具挑战。本文提出一种新框架，聚焦于纹理关键驱动因子（TKDF），即在不同情绪类别间具有强区分能力的局部纹理区域。通过观察面部图像模式，发现眉毛、眼睛和口周皮肤的微小变化是情绪动态的主要指示信号。为此，我们设计了包含纹理感知特征提取器（TAFE）和双上下文信息过滤器（DCIF）的FER架构。TAFE采用基于ResNet的主干网络，结合多分支注意力机制，以提取细粒度纹理表征；DCIF则通过自适应池化与注意力机制对特征进行上下文过滤优化。在RAF-DB和KDEF数据集上的实验结果表明，该方法取得当前最优性能，验证了将TKDF融入FER流程的有效性与鲁棒性。
原文摘要 · Abstract (English)
Facial expression recognition (FER) in the wild remains a challenging task due to the subtle and localized nature of expression-related features, as well as the complex variations in facial appearance. In this paper, we introduce a novel framework that explicitly focuses on Texture Key Driver Factors (TKDF), localized texture regions that exhibit strong discriminative power across emotional categories. By carefully observing facial image patterns, we identify that certain texture cues, such as micro-changes in skin around the brows, eyes, and mouth, serve as primary indicators of emotional dynamics. To effectively capture and leverage these cues, we propose a FER architecture comprising a Texture-Aware Feature Extractor (TAFE) and Dual Contextual Information Filtering (DCIF). TAFE employs a ResNet-based backbone enhanced with multi-branch attention to extract fine-grained texture representations, while DCIF refines these features by filtering context through adaptive pooling and attention mechanisms. Experimental results on RAF-DB and KDEF datasets demonstrate that our method achieves state-of-the-art performance, verifying the effectiveness and robustness of incorporating TKDFs into FER pipelines.
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