MASK FACE INPAINTING BASED ON IMPROVED GENERATIVE ADVERSARIAL NETWORK
نویسندگان
چکیده
Face recognition technology has been widely used in all aspects of people's lives. However, the accuracy face is greatly reduced due to obscuring objects, such as masks and sunglasses. Wearing public a crucial approach preventing illness, especially since Covid-19 outbreak. This poses challenges applications recognition. Therefore, removal via image inpainting become hot topic field computer vision. Deep learning-based techniques have taken observable results, but restored images still problems blurring inconsistency. To address problems, this paper proposes an improved model based on generative adversarial network: adds attention mechanisms sampling module pix2pix network; residual by adding convolutional branches. The can not only effectively restore faces obscured masks, also realize randomly human faces. further validate generality model, tests are conducted datasets CelebA, Paris Street Place2, experimental results show that both SSIM PSNR significantly.
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ژورنال
عنوان ژورنال: Applied Computer Science
سال: 2023
ISSN: ['1895-3735']
DOI: https://doi.org/10.35784/acs-2023-12