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4 May 2022 A makeup transfer method based on attention generative adversarial networks
Zhi Long, Xiafu Lv
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Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 121720N (2022) https://doi.org/10.1117/12.2634720
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China
Abstract
The images generated by the existing makeup transfer methods have some problems, such as large loss of facial structure and inconsistent color distribution with reference images. In this paper, a makeup transfer method is proposed, which can keep facial structure information unchanged. Firstly, this paper builds an efficient generator model by using the characteristics of U-Net which combines up-sampling and down-sampling information to extract image features and SE-Net which emphasizes useful features and suppresses useless features. At the same time, a loss function is designed to constrain the facial color distribution of the generated image so that the generated image is as consistent as possible with the color distribution of the reference image. Experiments show that the makeup transfer method in this paper not only can capture the color distribution of the reference image's face, but also better preserves the facial features of target image with an average SSIM of 0.8740.
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Zhi Long and Xiafu Lv "A makeup transfer method based on attention generative adversarial networks", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 121720N (4 May 2022); https://doi.org/10.1117/12.2634720
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KEYWORDS
Convolution
Distributed interactive simulations
Color difference
Image quality
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