Facial expression recognition (FER) is currently a very attractive research field in cognitive psychology and artificial intelligence. In this paper, an innovative FER algorithm called deep action units graph network (DAUGN) is proposed based on psychological mechanism. First, a segmentation method is designed to divide the face into small key areas, which are then converted into corresponding AU-related facial expression regions. Second, the local appearance features of these critical regions are extracted for further action units (AUs) analysis. Then, an AUs facial graph is constructed to represent expressions by taking the AU-related regions as vertices and the distances between each two landmarks as edges. Finally, the adjacency matrices of facial graph are put into a graph-based convolutional neural network to combine the local-appearance and global-geometry information, which greatly improving the performance of FER. Experiments and comparisons on CK+, MMI, and SFEW data sets reveal that the DAUGN achieves more competitive results than several other popular approaches.

中文翻译：

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基于心理机制的深度动作单元图网络面部表情识别

面部表情识别（FER）是目前认知心理学和人工智能中一个非常有吸引力的研究领域。在本文中，基于心理机制，提出了一种称为深度动作单元图网络（DAUGN）的创新 FER 算法。首先，设计了一种分割方法，将人脸划分为小的关键区域，然后将其转换为相应的与 AU 相关的面部表情区域。其次，提取这些关键区域的局部外观特征以进行进一步的动作单元 (AU) 分析。然后，通过将 AU 相关区域作为顶点，将每两个地标之间的距离作为边来构建 AU 人脸图来表示表情。最后，将人脸图的邻接矩阵放入基于图的卷积神经网络中，结合局部外观和全局几何信息，大大提高了FER的性能。对 CK+、MMI 和 SFEW 数据集的实验和比较表明，DAUGN 比其他几种流行方法获得了更具竞争力的结果。