Face super-resolution reconstruction method capable of recovering identity information

Abstract

The invention discloses a face super-resolution reconstruction method capable of recovering identity information, which comprises the following steps: 1) preparing training data including a low-resolution input image, a high-resolution target image and a target face mask; 2) constructing a face super-resolution network, wherein the face super-resolution network is composed of a main branch and an auxiliary branch which cooperate with each other; 3) sending a low-resolution image into the network in the step 2) to obtain a high-resolution image and a face mask output by a secondary branch; 4) calculating total loss, wherein the total loss comprises mask pixel loss, face mask loss and perception feature loss; 5) optimizing and updating network parameters according to the total loss in the step 4); 6) verifying whether the network performance in the step 5) reaches the standard or not, and if yes, stopping training; and if not, repeating the steps 1)-6). According to the method, the problem that the recognition rate of the face image generated by the existing face super-resolution reconstruction method is low is solved.

Description

technical field [0001] The invention relates to the technical field of face image reconstruction, in particular to a face super-resolution reconstruction method capable of recovering identity information. Background technique [0002] Face recognition is an identification technology based on facial biometric information. It is widely used in security systems, public places, traffic entrances and exits, community access control and other places of life and work. Compared with biological characteristics such as fingerprints, irises, and voices, face recognition has natural advantages such as non-contact, directness, and concealment, so it is the first choice for identity recognition applications. However, due to the hardware conditions of the imaging equipment and the constraints of the light environment when shooting, the collected face images often cannot meet the high-resolution requirements, which leads to the accuracy rate of the existing face recognition algorithms under...

AI Technical Summary

Problems solved by technology

Because although the existing methods reconstruct a seemingly good high-resolution face image, they do not recover the details of identity features and other information well.

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