Model training method, face recognition method and device, equipment and medium

Abstract

The invention discloses a model training method, a face recognition method and device, equipment and a medium, and relates to the technical field of artificial intelligence such as computer vision anddeep learning. According to the specific implementation scheme, the method comprises the steps of: obtaining at least two training data sets, wherein each training data set comprises face sample datawith the same number, and the types of the face sample data in different training data sets are different; performing face prediction on each training data set by using a pre-established face recognition model to obtain a face prediction result of each training data set; and performing supervised training on the face recognition model in parallel for the face prediction result of each training data set by utilizing pre-labeled face labeling data of each training data set. The face recognition model obtained by training can improve the face recognition accuracy of the sample data with less data distribution.

Description

technical field [0001] This application relates to the field of artificial intelligence, in particular to computer vision and deep learning technology, and specifically to a model training method, face recognition method, device, equipment and media. Background technique [0002] The current face recognition model needs to be trained with a large amount of face data, but the distribution of the training data is uneven. For example, the data of adults accounts for the vast majority, while the data of the elderly, children and foreigners are less. [0003] However, due to the uneven distribution of training data, it is easy to cause the face recognition effect of the trained model to be unbalanced, and the recognition effect on faces with less training data is poor. For example, when there are few foreigner data in the training data, it is easy to As a result, the trained model has a poor recognition effect on foreigners, which affects the accuracy of face recognition. Conte...

AI Technical Summary

Problems solved by technology

[0002] The current face recognition model needs to be trained with a large amount of face data, but the distribution of the training data is uneven. For example, the data of adults accounts for the vast majority, and the data of the elderly, children and foreigners are less.

[0003] However, due to the uneven distribution of training data, it is easy to cause the face recognition effect of the trained model to be unbalanced, and the recognition effect on faces with less training data is poor. For example, when there are few foreigner data in the training data, it is easy to As a result, the trained model has a poor recognition effect on foreigners, which affects the accuracy of face recognition

Images