Face age synthesis method and system

Abstract

The invention relates to a face age synthesis method and system, and relates to the technical field of face synthesis, and the method comprises the steps of carrying out the preprocessing of a test image, inputting the test image into a face age synthesis model, and obtaining face images of different age groups, wherein the training of the face age synthesis model comprises the step of optimizingparameters of a generator network by utilizing a loss function of the generator network according to an attention mask and a synthesized face image, taking the synthesized face image and the corresponding original input as negative samples; taking randomly selected face images in the randomly generated target age labels during synthesis as positive samples and inputting same into the discriminatornetwork to obtain the authenticity probabilities of the corresponding face images, and optimizing parameters of the discriminator network through a loss function of the discriminator network according to the probabilities; and when the number of iterations reaches the maximum number of iterations, obtaining a trained face age synthesis model. According to the invention, the visual quality of faceage synthesis can be improved.

Description

technical field [0001] The present invention relates to the technical field of human face synthesis, in particular to a method and system for human face age synthesis. Background technique [0002] Most of the existing methods for face age synthesis utilize a fully convolutional network with an hourglass structure as the generator. Such generators usually consist of two parts: an encoder and a decoder. First, the encoder encodes the input face into a latent representation. Then, the decoder receives latent representation and age label as input to implement age transfer. It should be noted that the target image is generated step by step, and each step utilizes the information generated by the previous step. Therefore, the intermediate feature representation largely affects the final generation. However, previous work usually uses the residual block as a bottleneck layer, modeling the common structure of the input and output. It should be noted that both face and latent f...

AI Technical Summary

Problems solved by technology

For example, existing methods may have the following problems: although the aging details of generated faces are relatively obvious, the visual quality is severely damaged due to the loss of some important information (e.g., hair, background, and face center)

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