Similar Graph Face Clustering Based on Graph Convolutional Neural Network

Abstract

Abstract Face clustering is primarily a method for grouping a large number of face images and has important applications in the fields of label-free face image annotation and image management. Traditional machine learning clustering algorithms do not work well on face image data and are unable to learn effectively on complex face image features. Recent research has turned to the use of graph convolutional neural networks (GCNs) to learn contextual information from neighbourhood features between face images for inference, which can significantly improve performance. Unlike the conventional link prediction of face data points and confidence prediction between vertices by using GCNs, for this paper a similarity graph based face clustering algorithm is proposed. Construction of subgraphs by K-nearest neighbour algorithm, Learn the similarity between the k-nearest neighbor subgraphs of each face instance by constructing subgraphs with the k-nearest neighbor algorithm. Using a bottom-up clustering strategy to merge subgraphs to complete the clustering, this algorithm improves the clustering results on complex face features by 20% and 7% compared to traditional machine learning methods such as K-means and DBSCAN respectively, improving the clustering accuracy and reducing the computational complexity.