| Summary: | Facial expression recognition (FER) in the wild from various viewpoints, lighting conditions, face poses, scales, and occlusions is an extremely challenging field of research. In this study, we construct a face graph by selecting action units that play an important role in changing facial expressions, and we propose an algorithm for recognizing facial expressions using a graph convolutional network (GCN). We first generated an attention map that can highlight action units to extract important facial expression features from faces in the wild. After feature extraction, a face graph is constructed by combining the attention map with face patches, and changes in expression in the wild are recognized using a GCN. Through comparative experiments conducted using both lab-controlled and wild datasets, we prove that the proposed method is the most suitable FER approach for use with image datasets captured in the wild and those under well-controlled indoor conditions.
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