As an important part of human-computer interactions, facial expression recognition has become a popular research topic in computer vision, pattern recognition, artificial intelligence and other fields. With the development of deep learning and convolutional neural networks, research on facial expression recognition has also made considerable progress. Because facial expressions vary in real environments, such as rotation, shifting, brightness changes, partial occlusion and noise with different intensities, research on the robustness of facial expression recognition is very important. A capsule network consists of capsules, which are groups of neurons, and these capsules can learn posture information through the dynamic routing mechanism. The length of a capsule represents the existence probability, and each neuron in a capsule represents posture information (e.g., position, size, orientation or a combination of these properties). Therefore, in this study, the robustness of the emerging capsule network (CapsNet) is comprehensively compares with that of the traditional convolutional neural network (CNN) and fully convolutional network (FCN) in facial expression recognition tasks. The simulation results based on the Cohn-Kanade (CK+) databases show that the capsule network is more robust than the other networks. Therefore, the capsule network has significant advantages over the other networks in facial expression recognition task in complex real-world environments.

面部表情识别作为人机交互的重要组成部分，已成为计算机视觉，模式识别，人工智能等领域的热门研究课题。随着深度学习和卷积神经网络的发展，面部表情识别的研究也取得了长足的进步。由于面部表情在真实环境中会发生变化，例如旋转，移动，亮度变化，部分遮挡和强度不同的噪音，因此研究面部表情识别的鲁棒性非常重要。胶囊网络由作为神经元组的胶囊组成，这些胶囊可以通过动态路由机制学习姿势信息。胶囊的长度代表存在概率，胶囊中的每个神经元代表姿势信息（例如，位置，大小，方向或这些属性的组合）。因此，在这项研究中，新兴的胶囊网络（CapsNet）的鲁棒性在面部表情识别任务中与传统的卷积神经网络（CNN）和全卷积网络（FCN）进行了全面比较。基于Cohn-Kanade（CK +）数据库的仿真结果表明，胶囊网络比其他网络更健壮。因此，在复杂的现实环境中，胶囊网络在面部表情识别任务中具有优于其他网络的显着优势。在面部表情识别任务中，新兴胶囊网络（CapsNet）的鲁棒性与传统卷积神经网络（CNN）和完全卷积网络（FCN）的鲁棒性进行了全面比较。基于Cohn-Kanade（CK +）数据库的仿真结果表明，胶囊网络比其他网络更健壮。因此，在复杂的现实环境中，胶囊网络在面部表情识别任务中具有优于其他网络的显着优势。在面部表情识别任务中，新兴胶囊网络（CapsNet）的鲁棒性与传统卷积神经网络（CNN）和完全卷积网络（FCN）的鲁棒性进行了全面比较。基于Cohn-Kanade（CK +）数据库的仿真结果表明，胶囊网络比其他网络更健壮。因此，在复杂的现实环境中，胶囊网络在面部表情识别任务中具有优于其他网络的显着优势。