Distributed large-scale face clustering method and device

Abstract

The embodiment of the invention discloses a distributed large-scale face clustering method and device. The embodiment of the invention provides a technical scheme. The method comprises the following steps: clustering to-be-clustered face pictures in batches and summarizing clustering results; obtaining a clustering set and a non-clustering set; extracting a set number of face pictures from each class of the clustering set to form a representative class, calculating the similarity distance between each unclustered face picture and each representative class; and obtaining a similarity distance set, clustering the unclustered human face images into the most similar representative classes based on the similarity distance set, determining a link relationship between each unclustered human faceimage and the corresponding representative class according to a set link threshold, combining the representative classes based on the link relationship, and outputting a combination result. By adopting the technical means, the data calculation amount of large-scale face data clustering can be reduced, the memory consumption is reduced, and the face clustering efficiency is improved on the premiseof ensuring the stability of the clustering result.

Description

technical field [0001] The embodiments of the present application relate to the technical field of data clustering, and in particular to a distributed large-scale face clustering method and device. Background technique [0002] At present, in many business scenarios of intelligent security, it is necessary to cluster a large amount of unlabeled face data to simplify the face data labeling process or achieve other data processing purposes. The core problem of face clustering is to divide the collected N unlabeled photos into M individuals, and "M" is unknown in advance. Most of the face clustering algorithms obtain face pictures, use the existing feature extraction technology to extract the feature vector of the face picture, and cluster all the face pictures to be clustered according to the extracted feature vector and a certain clustering algorithm . When the clustering algorithm clusters the clustered face pictures, it often needs to compare the face features to obtain t...

AI Technical Summary

Problems solved by technology

When the magnitude of face data is large, the computational complexity of data clustering increases accordingly, and a large amount of storage space is required to store the similarity value of any two face data

Its data clustering takes a relatively long time, and the increase in memory consumption will lead to insufficient system memory, further affecting the efficiency of face data clustering

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