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...

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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

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