Face clustering method and device based on graph convolution and electronic equipment

Abstract

The invention provides a face clustering method and device based on graph convolution and electronic equipment, and relates to the technical field of face clustering, and the method comprises the steps: obtaining a plurality of pieces of to-be-clustered face data, extracting the face features of the plurality of pieces of to-be-clustered face data, constructing a first K-nearest neighbor graph based on the extracted face features, and obtaining a first K-nearest neighbor graph based on the first K-nearest neighbor graph; and inputting the first K neighbor graph into the trained graph convolutional network to obtain a density prediction value of each node in the first K neighbor graph, performing directed connection on the node with the low density prediction value and the node with the high density prediction value and the maximum connection strength, and constructing a plurality of independent face clusters. The mode of combining the graph convolutional neural network and the clustering algorithm based on the density predicted value does not need to additionally construct a sub-graph, and the operation efficiency and accuracy of the clustering algorithm are effectively improved.

Description

technical field [0001] The present invention relates to the technical field of face clustering, in particular to a face clustering method and device based on graph convolution. Background technique [0002] In recent years, with the rapid development of face detection and face recognition technology, face images can be obtained more quickly from the Internet or surveillance cameras. For a large number of acquired unlabeled face images, the cost of manual grouping is high and grouping errors are prone to occur, so there is a need for automatic grouping of face images. Face clustering is a practical tool with wide application, which can automatically classify and group a large amount of unlabeled face data, effectively saving the cost of data collation. [0003] Traditional clustering methods such as k-means (k-means clustering algorithm) and DBSCAN (Density-Based Spatial Clustering of Applications) algorithms rely on specific assumptions, lack the ability to deal with comple...

AI Technical Summary

Problems solved by technology

[0003] Traditional clustering methods such as k-means (k-means clustering algorithm) and DBSCAN (Density-Based Spatial Clustering of Applications) algorithms rely on specific assumptions, lack the ability to deal with complex clustering structures of real data sets, and cluster accurately Low rate

Clustering methods based on deep learning usually need to construct a large number of subgraphs to realize the connection prediction between nodes, which seriously affects the efficiency of clustering

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