Face clustering method and device based on structure perception

Abstract

The invention provides a face clustering method and device based on structure perception, and the method comprises the steps: obtaining a plurality of to-be-processed face images, extracting the face features of each to-be-processed face image based on a pre-trained convolutional neural network model, and constructing a K-nearest neighbor graph according to the face features of each to-be-processed face image; inputting the K neighbor graph into a pre-trained edge score prediction model to obtain the score of each edge in the K neighbor graph, wherein the edge score prediction model is obtained by sampling a K neighbor graph by using a structure retention sub-graph sampling strategy and training a graph convolutional neural network by using a sub-graph obtained by sampling; and according to the score of each edge in the K neighbor graph, performing a first pruning operation on the K neighbor graph to obtain a face cluster for the plurality of to-be-processed face images. The technical problem of insufficient face clustering accuracy in the prior art is solved.

Description

technical field [0001] The present application relates to the field of artificial intelligence and deep learning technology in the field of image processing technology, and in particular to a face clustering method and device based on structure perception. Background technique [0002] The development of face recognition technology relies on the proposal of large-scale face datasets. In recent years, with the development of face recognition technology, the size of face datasets has become larger and larger. However, with the increase in the size of datasets, the cost of labeling has become more and more expensive. [0003] The face clustering algorithm is an effective method to reduce the cost of labeling. However, in related technologies, when faced with large-scale real face data, the clustering accuracy of the clustering model needs to be improved. Contents of the invention [0004] This application aims to solve one of the technical problems in the related art at leas...

AI Technical Summary

Problems solved by technology

In recent years, with the development of face recognition technology, the size of face datasets has become larger and larger. However, with the increase in the size of datasets, the cost of labeling has become more and more expensive.

[0003] The face clustering algorithm is an effective way to reduce the cost of labeling. However, in related technologies, when faced with large-scale real face data, the clustering accuracy of the clustering model needs to be improved.

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