The task of facial expression recognition (FER) is riddled with many challenges, such as face occlusion, head posture, illumination angle, and intensity. Due to the development of deep learning and large FER datasets in recent years, most methods have achieved notable success. This paper aims to solve the problem that general classification models are difficult to distinguish, for some easily confused expressions (such as anger and surprise). To this end, we make two contributions in this paper: (1) The model extracts weighted local key regions as local information on the final feature maps, and fuses the global information for multi-task recognition. (2) Triplet loss function is used to make the intra-class feature distance significantly reduced from the inter-class feature distance. It can enhance the discriminability of features while fitting the sample distribution. The experiments confirm that two contributions are combined to gain another round of performance boost. For instance, the results on CK+ and FER2013 datasets demonstrate the superiority of the proposed method.

中文翻译：

---

LKRNet：基于局部关键区域的人脸表情识别双分支网络

面部表情识别（FER）的任务充满了许多挑战，例如面部遮挡、头部姿势、照明角度和强度。由于近年来深度学习和大型 FER 数据集的发展，大多数方法都取得了显着的成功。本文旨在解决一般分类模型难以区分的问题，对于一些容易混淆的表情（如愤怒和惊讶）。为此，我们在本文中做出了两个贡献：（1）该模型提取加权的局部关键区域作为最终特征图上的局部信息，并融合全局信息进行多任务识别。(2)使用Triplet loss函数使类内特征距离相对于类间特征距离显着减小。它可以在拟合样本分布的同时增强特征的可辨别性。实验证实，结合两个贡献以获得另一轮性能提升。例如，在 CK+ 和 FER2013 数据集上的结果证明了所提出方法的优越性。