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.
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This work has been partially supported by Heilongjiang Science Foundation Project (LH2021F052).
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Liu, F., Chen, R., Duan, S., Hao, M., Guo, Y. (2022). Research on Face Image Restoration Based on Improved WGAN. In: Jiang, X. (eds) Machine Learning and Intelligent Communications. MLICOM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-031-04409-0_13
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DOI: https://doi.org/10.1007/978-3-031-04409-0_13
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