Data processing method and device and storage medium

Abstract

The embodiment of the invention discloses a data processing method and device and a storage medium, wherein the method comprises the steps of separately obtaining the sample object data from multiplepieces of multimedia data, and extracting the object feature information of the sample object data; clustering the plurality of sample object data according to the object feature information to obtaina plurality of first object feature clusters with different cluster labels; cleaning the heterogeneous object data in each first object feature cluster, and determining the cleaned first object feature cluster as a second object feature cluster; performing similar cluster merging among the plurality of second object feature clusters, generating a plurality of third object feature clusters with different cluster labels, and updating the object labels of the sample object data into cluster labels of the third object feature clusters to which the object labels belong; and based on the object label and the object feature information of the sample object data in each third object feature cluster, training an object recognition model. According to the invention, the accuracy of the face recognition can be improved.

Description

technical field [0001] The present invention relates to the field of electronic technology, in particular to a data processing method, device and storage medium. Background technique [0002] With the continuous promotion of face recognition technology in practical applications, the problems and difficulties of face recognition have gradually become prominent. In an unreliable collection environment, the user's facial expression and environmental status are ever-changing, and its complexity is far beyond The pictures obtained in the standard evaluation, for example, occlusion will lead to changes in facial features, which will increase the difficulty of face recognition and increase the rejection rate; changes in lighting, posture and expression will also lead to changes in a person's different environments. Facial features vary greatly, which reduces the recognition rate and may increase false recognition. At present, in order to improve the performance of face recognition...

AI Technical Summary

Problems solved by technology

[0002] With the continuous promotion of face recognition technology in practical applications, the problems and difficulties of face recognition have gradually become prominent. In an unreliable collection environment, the user's facial expression and environmental status are ever-changing, and its complexity is far beyond The pictures obtained in the standard evaluation, for example, occlusion will lead to changes in facial features, which will increase the difficulty of face recognition and increase the rejection rate; changes in lighting, posture and expression will also lead to changes in a person's different environments. Facial features are very different, which reduces the recognition rate and may increase false recognition

At present, in order to improve the performance of face recognition technology in practical application scenarios, the most direct and effective method is to increase a large amount of face training data with identity labels, and there are a large number of real-world scenarios (such as monitoring, shopping malls, communities, etc.) face data, but most of them are face data without identity tags, and it is impossible to obtain a large amount of face training data with identity tags, so it is impossible to improve face recognition performance by increasing face training data

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