Facial expression recognition (FER) is a significant research task in the computer vision field. In this paper, we present a novel network FaceCaps for facial expression recognition with the following novel characteristics: an embedding structure based on a Capsule network which encodes relative spatial relationships between features; incorporates the feature polymerization property of FaceNet, thus offering a more efficient approach to discriminate complex facial expressions; a target reconstruction loss as a better regularization term for Capsule networks. Experimental results on both lab-controlled datasets (CK+) and real-world databases (RAF-DB and SFEW 2.0) demonstrate that the method significantly outperforms the state-of-the-art.

中文翻译：

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FaceCaps 用于面部表情识别

面部表情识别（FER）是计算机视觉领域的一项重要研究任务。在本文中，我们提出了一种用于面部表情识别的新型网络 FaceCaps，具有以下新特性：基于 Capsule 网络的嵌入结构，该网络编码特征之间的相对空间关系；结合了 FaceNet 的特征聚合特性，从而提供了一种更有效的方法来区分复杂的面部表情；目标重建损失作为胶囊网络的更好的正则化项。在实验室控制的数据集 (CK+) 和真实世界数据库（RAF-DB 和 SFEW 2.0）上的实验结果表明，该方法明显优于最先进的方法。