Face image generation method and device, server and storage medium

Abstract

The embodiment of the invention discloses a face image generation method and device, a server and a storage medium. The method comprises the steps of obtaining a face image of a target person and an expression label corresponding to the face image; performing face detection on the face image to obtain a standard face image of the face image; performing expression synthesis according to the standard face image and the expression label by utilizing an expression generation model to obtain a first synthesized face image; performing face synthesis according to the standard face image and the firstsynthetic face image to obtain a second synthetic face image; and generating a face image including the second synthetic face image. By adopting the method and the device, the generated expression can be more stable, and the editing requirements of the user on the expression, particularly micro-expression, can be met. The invention further relates to the blockchain technology, the index information of the face image including the second synthetic face image can be written into the block chain, and meanwhile, the invention further relates to an image processing technology in the field of artificial intelligence.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a face image generation method, device, server and storage medium. Background technique [0002] Deep learning is a popular technology research field in recent years, and related applications are emerging in an endless stream. For example, the application of Generative Adversarial Networks (GAN) generally includes generating images of different styles, completing images, generating high-definition data for data enhancement of other machine learning models, and so on. [0003] GAN's extensive and interesting applications and high technical threshold have made GAN the focus of research by major technology companies and universities. Here, we focus on the application of GAN to expression generation. The traditional method of locally adjusting character images through the GAN model has the disadvantage of generating unstable expressions, especially in wild scenes. ...

AI Technical Summary

Problems solved by technology

The traditional method of locally adjusting character images through the GAN model has the disadvantage of generating unstable expressions, especially in wild scenes.

In addition, the number of expressions generated by the traditional method is very limited, and generally only supports a few types of expressions, which cannot meet the user's editing needs for expressions, especially micro-expressions

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