Face data amplification method based on generative adversarial network, electronic equipment and storage medium

Abstract

The invention provides a face data amplification method based on a generative adversarial network, electronic equipment and a storage medium, and the method comprises the steps: obtaining a face image data set, and carrying out the preprocessing of a face image; classifying the face pictures according to classification attributes, and marking category labels to serve as a face picture training set; constructing a GAN model; inputting the face picture training set into the constructed GAN model for training to obtain a trained GAN model; inputting the face sample pictures with the category labels into the trained GAN model, and automatically generating face pictures including new features; and respectively extracting features from the face image including the new features and the face sample image marked with the category label, and removing deviation data to obtain a face newly-added data set. More diversified face data can be generated, so that sample data is expanded, and the robustness of a face recognition model is improved.

Description

technical field [0001] The present disclosure relates to the technical field of medical databases, in particular to a face data augmentation method based on a generative confrontation network, an electronic device and a storage medium. Background technique [0002] Rare diseases are characterized by high incidence and difficult diagnosis in my country. With the widespread application of artificial intelligence in the medical field, people have also begun to study how to use artificial intelligence to detect and diagnose rare diseases in children. It is of great significance to detect high-risk groups of rare diseases in time, so as to further diagnose and achieve early detection and early treatment, which is of great significance for improving the diagnosis and treatment effect of rare diseases. Relevant studies have shown that many rare diseases are related to facial features, so analyzing and dissecting facial features is of great significance for early screening of rare ...

AI Technical Summary

Problems solved by technology

Adding noise and other methods to perform sample amplification, for medical sample pictures, this often leads to the disappearance of original features, or adds other interference features

In addition, new samples also need to be relabeled and tested, and the workload is heavy.

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