GLaRE：用于情绪识别的基于图的关键区域嵌入网络
GLaRE: A Graph-based Landmark Region Embedding Network for Emotion Recognition
AI总结：
本文提出GLaRE，利用3D面部关键点和层次化粗化商图学习区域级嵌入，以提升面部表情识别的准确率与可解释性，并在AffectNet和FERG上优于多个基线。
摘要：
面部表情识别（FER）是计算机视觉中的一项关键任务，具有广泛应用，包括人机交互、监控和辅助技术。然而，遮挡、表情变化以及缺乏可解释性等挑战阻碍了传统FER系统的性能。图神经网络（GNN）通过对面部关键点之间的关系依赖进行建模，提供了一种强大的替代方案，能够实现结构化且可解释的学习。本文提出GLaRE，这是一种新颖的用于情绪识别的基于图的关键区域嵌入网络。该方法使用3D面部对齐提取面部关键点，并通过层次化粗化构造商图，以在降低复杂度的同时保留空间结构。我们的方法在AffectNet上达到64.89%的准确率，在FERG上达到94.24%的准确率，优于若干现有基线。此外，消融研究表明，来自商图的区域级嵌入有助于提升预测性能。
英文摘要：
Facial expression recognition (FER) is a crucial task in computer vision with wide range of applications including human computer interaction, surveillance, and assistive technologies. However, challenges such as occlusion, expression variability, and lack of interpretability hinder the performance of traditional FER systems. Graph Neural Networks (GNNs) offer a powerful alternative by modeling relational dependencies between facial landmarks, enabling structured and interpretable learning. In this paper, we propose GLaRE, a novel Graph-based Landmark Region Embedding network for emotion recognition. Facial landmarks are extracted using 3D facial alignment, and a quotient graph is constructed via hierarchical coarsening to preserve spatial structure while reducing complexity. Our method achieves 64.89 percentage accuracy on AffectNet and 94.24 percentage on FERG, outperforming several existing baselines. Additionally, ablation studies have demonstrated that region-level embeddings from quotient graphs have contributed to improved prediction performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
