Research Article
Research on Face Image Restoration Based on Improved WGAN
@INPROCEEDINGS{10.1007/978-3-031-04409-0_13, author={Fugang Liu and Ran Chen and Songnan Duan and Mingzhu Hao and Yang Guo}, title={Research on Face Image Restoration Based on Improved WGAN}, proceedings={Machine Learning and Intelligent Communications. 6th EAI International Conference, MLICOM 2021, Virtual Event, November 2021, Proceedings}, proceedings_a={MLICOM}, year={2022}, month={5}, keywords={WGAN Face recognition Face image inpainting}, doi={10.1007/978-3-031-04409-0_13} }- Fugang Liu
Ran Chen
Songnan Duan
Mingzhu Hao
Yang Guo
Year: 2022
Research on Face Image Restoration Based on Improved WGAN
MLICOM
Springer
DOI: 10.1007/978-3-031-04409-0_13
Abstract
This article focuses on the face recognition model in real life scenarios, because the possible occlusion affects the recognition effect of the model, resulting in a decline in the accuracy of the model. An improved WGAN network is proposed to repair occluded facial images. The generator in the improved WGAN network is composed of an encoder-decoder network, and a jump connection is used to connect the bottom layer with the high-level feature information to generate missing facial images. The low-level feature information is connected with the deep-level feature information, and the network's ability to extract features and generate pictures is enhanced at the same time. The paper also uses a global discriminator and a local discriminator, taking all the restored pictures as input to measure the overall authenticity, and taking the restored part of the pictures as input to judge whether the content structure is reasonable. After comparison and analysis of experiments, the improved face image has a complete structure and clear content, which is helpful for face recognition with partial occlusion.
