Face image age conversion method based on gradient adversarial attack and generative adversarial model

Abstract

The invention discloses a face image age conversion method based on gradient adversarial attack and a generative adversarial model, and the method mainly comprises the steps: firstly aligning face images, intercepting the face parts of the face images, obtaining an original image through the rotation of the images, adjusting the angle, and giving an expected target face age; initializing an implicit space vector, and inputting the implicit space vector into a decoder to obtain a generated picture; according to the algorithm, using an age estimator, a discriminator and a face recognizer to obtain the age and the trueness of a generated picture and the loss of the identity similarity between the generated picture and an original picture face; and with loss reduction as a target, carrying out gradient updating on the initialized hidden space vector, and then repeating the first two steps until the step length agreed by the algorithm is reached. By using the method, automatic conversion of face pictures can be realized, criminal investigation and cross-age face recognition are helped, and the invention can also be used for entertainment and movie and television.

Description

technical field [0001] The invention belongs to the field of image synthesis and translation, and in particular relates to an age conversion method of a human face image. Background technique [0002] As society enters the digital age, people's film and television entertainment activities are gradually enriched. The age conversion of face pictures by artificial intelligence methods can be used as video special effects and widely used in live broadcast platforms and social media. Many film and television works require the process of shooting characters from childhood to adulthood, and the traditional manual age conversion special effects production is expensive and time-consuming, and the use of artificial intelligence methods can greatly reduce costs. Criminal investigation often encounters situations where prisoners or missing persons have not appeared for decades. To face this problem, the traditional method uses experts to manually draw age-transformed portraits, which t...

AI Technical Summary

Problems solved by technology

However, the training time of the model is extremely long, and the features extracted during the training process designed solely for face age conversion are not representative enough. The images generated by large-span age conversion are not very realistic, and the face identity, posture, background and illumination in the age conversion process difficult to maintain

Therefore, the existing face image age conversion technology needs to be improved

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