A graph convolutional network face recognition algorithm based on clustering

Graph Convolutional Networks (GCN) have emerged as effective models in graph data analysis and Deep Learning tasks, achieving significant advancements in recent years. In this paper, we propose GCN based on clustering algorithm for face recognition. By transforming the clustering problem into two sub-problems, two graph convolutional networks are employed, one graph convolutional network estimates the confidence of vertices in the graph, and another graph convolutional network estimates the connectivity of edges in the graph. This approach effectively captures both local and global information among face images. Experimental results demonstrate that the clustering-based GCN model achieves state-of-the-art performance on various public face datasets, highlighting the effectiveness and potential of clusteringbased GCN in face recognition tasks. These findings provide valuable insights for further improving and applying GCN models and serve as a beneficial reference in this field.