A facial expression conversion method based on identity and expression feature conversion

Abstract

The invention provides a facial expression conversion method based on identity and expression feature conversion, and mainly solves the problem of personalized facial expression. Most of the existingfacial expression synthesis work attempts to learn conversion between expression domains, so that paired samples and marked query images are needed. Identity information and expression feature information of an original image can be stored by establishing the two encoders, and target facial expression features are used as condition tags. The method mainly comprises the following steps: firstly, carrying out facial expression training, preprocessing a neutral expression picture and other facial expression pictures, then extracting identity characteristic parameters and target facial expressioncharacteristic parameters of a neutral expression, and establishing a matching model; Secondly, performing facial expression conversion, inputting the neutral expression picture into a conversion model, and applying model output parameters to expression synthesis to synthesize a target expression image. Pairing data sets of different expressions with the same identity are not limited any more, identity information of an original image can be effectively reserved due to existence of the two encoders, and conversion from a neutral expression to different expressions can be achieved.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a human facial expression conversion method based on identity and expression feature conversion. Background technique [0002] With the rapid development of computer technology, computers play an important role in human life. People also hope that computers can have human intelligence, and hope that computers can communicate with people unimpeded, and more natural human-computer interaction requires computers to have the ability to understand and express emotions, and to be able to adapt to the environment independently, which will fundamentally Changing the relationship between humans and computers, combining information such as facial expressions, voices, sights, and body postures can achieve more efficient and humanized human-computer interaction, and enable computers to better serve humans. Facial expression synthesis technology is an important aspect of computer simulation of ...

AI Technical Summary

Problems solved by technology

Researchers have strict requirements for the dataset, that is, facial images of the same person with different expressions, and some even require long-term paired samples, which are difficult to collect

Although the database contains a wide variety of image facial expressions, it is difficult to discern facial expressions and identity information due to the limited number of subjects

Images