Facial expression synthesis method based on generative adversarial network

Abstract

The invention discloses a facial expression synthesis method based on a generative adversarial network. The method comprises the following steps: firstly, acquiring data of facial expressions, preprocessing the facial expression data, cutting facial images into training data and test data, and manually classifying the training data into different categories according to different facial expressionintensities; then constructing a generative adversarial network, and adding judgment information of expression intensity and associated information between different expression intensities into the network; training and testing the generative adversarial network by using the preprocessed expression data, and adjusting network parameters to optimize the generative adversarial network model; and finally, selecting test data of neutral expressions and inputting the test data into the trained generative adversarial network model to obtain expression images with different intensities. According tothe method, the facial expression images with different intensities can be synthesized through the neutral expression-free facial images, the method is convenient and visual, and the synthesized expression intensity is more vivid and realistic.

Description

technical field [0001] The invention relates to the technical fields of deep learning and image processing, in particular to a method for synthesizing human facial expressions based on a generative confrontation network. Background technique [0002] Face image processing is a broad research topic in the fields of computer vision and graphics. Facial expression is not only a subtle form of body language, but also an important way for people to convey emotional information. In recent years, with the development of computer information technology and services, people increasingly hope that computer communication can show anthropomorphic emotions and provide a new sense of immersion in human-computer interaction, which also promotes the development of expression synthesis. . Facial expression synthesis has also become one of the current research hotspots, and has a wide range of applications such as human-computer interaction, virtual reality, digital entertainment and other ...

AI Technical Summary

Problems solved by technology

[0004] However, the first type of method cannot capture the change process of different expression intensities, such as facial wrinkles, etc., resulting in unnatural and unrealistic expressions

In addition, images generated by such methods sometimes lack fine details and are often blurred or low-resolution.

The second type of method requires that the data must be marked with expression intensity. However, in practical applications, it is difficult to define the intensity of expression with a unified standard. Although this type of method can achieve fine-grained control, it has limitations.

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