Face recognition method and system

Abstract

The invention relates to a face recognition method and system, and relates to the technical field of information, wherein the face recognition method and system mainly aim at completing face recognition through the smaller calculation amount compared with the prior art. The method includes the steps of clustering collected face images to obtain multiple first-layer classifications, continuing clustering face images in at least one ith-layer classification through the iterative way to obtain multiple (i+1)th-layer classifications, recognizing the first-layer classification to which a target face image belongs, continuing recognizing the (j+1)th-layer classification to which the target face image belongs in the jth-layer classification to which the target face image belongs through the iterative way, and recognizing a face image similar to the target face image from the jth-layer classification to which the target face image belongs through the iterative way till no (j+1)th-layer classification exists in the jth-layer classification to which the target face image belongs. Compared with an existing technical scheme, the calculation amount is quite small and face recognition efficiency is greatly improved.

Description

technical field [0001] The present invention relates to the field of information technology, in particular, to a face recognition method and system. Background technique [0002] At present, to retrieve large-scale faces, the work that needs to be done is to first extract and quantify the features of the face data. After these tasks, each face can get the corresponding multi-dimensional feature data; and by comparing two faces The feature data of the image is used to determine whether two face images are similar. In large-scale face data, finding similar faces requires calculations for each image, and finding the nearest face data requires a huge amount of calculations. [0003] The existing solution is to directly cluster a large number of face images in the local database, compare the target face image with each cluster in turn to find the cluster it belongs to, and then compare it with each face image in the cluster it belongs to. to find similar faces. This scheme spe...

AI Technical Summary

Problems solved by technology

This scheme speeds up the face search process to a certain extent, but in large-scale face search, this scheme still requires a huge amount of calculation: if there are fewer cluster settings, the data size in each cluster will be relatively large , searching in the clusters will consume a lot of computing time; if there are too many clusters, it will take a lot of calculation to find the clusters to which they belong, which cannot meet the real-time requirements of search engines

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