Face super-resolution reconstruction method based on identity prior generative adversarial network

Abstract

The invention relates to a face super-resolution reconstruction method based on an identity priori generative adversarial network. The face super-resolution reconstruction method comprises the following steps: firstly, reading an original face picture data set; then, utilizing a face image-identity label pair to train a face feature extraction network; thirdly, reading the high-resolution face image for bicubic interpolation down-sampling to obtain a high-resolution face image-low-resolution face image pair for model training; fourthly, inputting the low-resolution face image into a generatornetwork to generate a super-resolution face image; respectively inputting the high-resolution face image and the super-resolution face image into a trained face feature extraction network, and extracting identity prior features of the high-resolution face image and the super-resolution face image; and inputting the high-resolution face image, the super-resolution image and the corresponding identity prior features into a discriminator network, calculating a supervised adversarial loss function by using the output of the discriminator network, and training a generative adversarial network by using error back propagation.

Description

technical field [0001] The invention relates to the field of image reconstruction methods, in particular to a face super-resolution reconstruction method based on an identity prior generation confrontation network. Background technique [0002] With the continuous improvement of security standards in crowded areas such as airports, subways, and shopping centers, intelligent surveillance systems based on machine vision have received more and more attention. In order to obtain a wider field of view, most surveillance videos usually collect faces with a small resolution. Compared with clear, high-resolution face pictures, the discrimination and information content of small-scale face pictures are greatly reduced. Therefore, surveillance videos Face recognition systems need to perform super-resolution reconstruction operations on small-scale face images. The super-resolution reconstruction method can restore the texture details of face images and improve the accuracy of face re...

AI Technical Summary

Problems solved by technology

Generative adversarial networks can restore more realistic texture details, but the traditional unsupervised learning methods of generative adversarial networks make the generated images far from the original images, and cannot be used for face recognition in low-resolution images of surveillance videos. Therefore, it is necessary to target this problem to improve

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