Due to the problems of occlusion, pose change, illumination change, and image blur in the wild facial expression dataset, it is a challenging computer vision problem to recognize facial expressions in a complex environment. To solve this problem, this paper proposes a deep neural network called facial expression recognition based on graph convolution network (FERGCN), which can effectively extract expression information from the face in a complex environment. The proposed FERGCN includes three essential parts. First, a feature extraction module is designed to obtain the global feature vectors from convolutional neural networks branch with triplet attention and the local feature vectors from key point-guided attention branch. Then, the proposed graph convolutional network uses the correlation between global features and local features to enhance the expression information of the non-occluded part, based on the topology graph of key points. Furthermore, the graph-matching module uses the similarity between images to enhance the network’s ability to distinguish different expressions. Results on public datasets show that our FERGCN can effectively recognize facial expressions in real environment, with RAF-DB of 88.23%, SFEW of 56.15% and AffectNet of 62.03%.

由于野生面部表情数据集中存在遮挡、姿态变化、光照变化和图像模糊等问题，在复杂环境中识别面部表情是一个具有挑战性的计算机视觉问题。为了解决这个问题，本文提出了一种基于图卷积网络（FERGCN）的深度神经网络，称为面部表情识别，可以在复杂环境中有效地从人脸中提取表情信息。提议的 FERGCN 包括三个基本部分。首先，设计了一个特征提取模块，从具有三重注意力的卷积神经网络分支中获取全局特征向量，从关键点引导的注意力分支中获取局部特征向量。然后，所提出的图卷积网络基于关键点的拓扑图，利用全局特征和局部特征之间的相关性来增强非遮挡部分的表达信息。此外，图匹配模块利用图像之间的相似性来增强网络区分不同表情的能力。公共数据集的结果表明，我们的 FERGCN 可以有效识别真实环境中的面部表情，RAF-DB 为 88.23%，SFEW 为 56.15%，AffectNet 为 62.03%。