Recognizing emotion through facial expression has now been widely applied in our daily lives. Therefore, facial expression recognition (FER) is attracting increasing research interests in the field of artificial intelligence and multimedia. With the development of convolutional neural networks (CNN), end-to-end deep learning frameworks for FER have achieved great success on large-scale datasets. However, these works still face the problems of redundant information and data bias, which obviously decrease the performance of FER. In this article, we propose a novel multiscale graph convolutional network (GCN) based on landmark graphs extracted from facial images. The proposed method is evaluated on different popular datasets. The results show that the proposed method outperforms the traditional deep learning frameworks and achieves more stable performance on different datasets.

中文翻译：

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使用多尺度图卷积网络进行面部表情识别

通过面部表情识别情绪现已广泛应用于我们的日常生活中。因此，面部表情识别（FER）在人工智能和多媒体领域吸引了越来越多的研究兴趣。随着卷积神经网络 (CNN) 的发展，用于 FER 的端到端深度学习框架在大规模数据集上取得了巨大成功。然而，这些工作仍然面临信息冗余和数据偏差的问题，这明显降低了 FER 的性能。在本文中，我们提出了一种基于从面部图像中提取的地标图的新型多尺度图卷积网络 (GCN)。所提出的方法在不同的流行数据集上进行了评估。