基于图卷积神经网络的完全图人脸聚类

Complete Graph Face Clustering Based on Graph Convolution Network

人脸聚类是根据不同身份对人脸图像进行分组的方法,主要用于人脸标注和图像管理等领域。针对现有方法中存在大量冗余数据的问题,文中使用一种基于完全图约束和上下文关系进行链接预测的方法。该聚类算法基于图卷积神经网络进行链接预测,结合完全图约束筛选数据,同时在预测的过程中对链接关系进行不断的更新。实验结果显示,结合完全图约束的人脸聚类方法能够在减少冗余数据、加快运行速度的同时,提升聚类的准确率,从而提高聚类的整体效果。

Face clustering is a method of grouping face images according to different identities,which is mainly used in the fields of face annotation,image management.etc.There is massive redundant data in existing methods.To handle this issue,this paper uses a link prediction method based on complete graph constraint and context relationship.The clustering algorithm is based on graph convolution network for link prediction,combined with complete graph constraints to filter data,and the link relationship is constantly updated in the process of prediction.Experimental results show that the face clustering method combined with complete graph constraint can reduce redundant data,speed up the operation,and improve the accuracy of clustering.Thus it improves the overall performance of clustering.