Face attribute editing method based on generative adversarial network and information processing terminal

Abstract

The invention belongs to the technical field of human face attribute editing, discloses a human face attribute editing method based on a generative adversarial network and an information processing terminal, and constructs a human face attribute editing model combining the generative adversarial network and an auto-encoder. Wherein the auto-encoder is used as a generator, and the input of the model is pictures and attributes; optimizing the GAN loss by using the WGAN-GP to realize a task of editing a plurality of attributes by using a single generator; enabling the generated image to correctlyhave expected attributes by using an attribute classifier; a multi-scale discriminator is adopted to guide a generator to generate details, and detail information is captured on an original image; and combining the reconstruction loss, the attribute classification loss and the multi-scale GAN loss for face attribute editing. Experiments on a CelebA data set show that a high-quality face image isgenerated on the basis that expected attributes are correctly owned, and the method has good performance in the aspects of single-attribute face editing, multi-attribute face editing and attribute strength control.

Description

technical field [0001] The invention belongs to the technical field of face attribute editing, and in particular relates to a face attribute editing method based on a generative confrontation network and an information processing terminal. Background technique [0002] At present, face attribute editing is to edit facial images by modifying single or multiple attributes under the condition of maintaining the identity of the face. Face attribute editing is the task of changing a face image to a given attribute (for example, hair color, expression, beard and age, etc.), while ensuring the invariance of face identity information and attribute-independent regions, generating images with desired attributes and retaining them. Detailed new face images, related research has been widely used in entertainment, social, facial animation, expression recognition and other fields, such as whitening, age reduction, smiling, etc., in the field of face recognition. Augmentation has received...

AI Technical Summary

Problems solved by technology

[0006] The difficulty of solving the above problems and defects is: difficult (the receptive field of a single discriminator is very limited, especially when the size of the training picture becomes larger, which will affect the extraction of information)

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