Abstract
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.
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