通过融合外观与面部区域关系，提升表情识别精度。
ARPGNet: Appearance- and Relation-aware Parallel Graph Attention Fusion Network for Facial Expression Recognition
- 并行图注意力模块同时学习面部外观和区域间关系
- 在三个数据集上表现优于或接近当前最优方法
- 适合关注动态表情建模与跨区域交互的研究者
表情识别的关键在于学习能表征面部表情动态的判别性时空特征。以往研究多依赖预训练卷积神经网络（CNN）提取面部外观特征，忽视了面部区域间的相互关系。为此，本文提出外观与关系感知的并行图注意力融合网络（ARPGNet），以学习外观与关系信息之间的相互增强的时空表示。具体而言，构建面部区域关系图，并利用图注意力机制建模区域间关系。得到的关系表示序列与基于CNN的外观表示序列共同输入并行图注意力融合模块，实现双向交互与增强。该模块同时探索不同表示序列间的互补性及每条序列内的时序动态。在三个表情识别数据集上的实验表明，所提ARPGNet优于或相当于当前最优方法。
原文摘要 · Abstract (English)
The key to facial expression recognition is to learn discriminative spatial-temporal representations that embed facial expression dynamics. Previous studies predominantly rely on pre-trained Convolutional Neural Networks (CNNs) to learn facial appearance representations, overlooking the relationships between facial regions. To address this issue, this paper presents an Appearance- and Relation-aware Parallel Graph attention fusion Network (ARPGNet) to learn mutually enhanced spatial-temporal representations of appearance and relation information. Specifically, we construct a facial region relation graph and leverage the graph attention mechanism to model the relationships between facial regions. The resulting relational representation sequences, along with CNN-based appearance representation sequences, are then fed into a parallel graph attention fusion module for mutual interaction and enhancement. This module simultaneously explores the complementarity between different representation sequences and the temporal dynamics within each sequence. Experimental results on three facial expression recognition datasets demonstrate that the proposed ARPGNet outperforms or is comparable to state-of-the-art methods.
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