A Novel Efficient Deep Learning Framework for Facial Inpainting
2023 IEEE Conference on Artificial Intelligence (CAI)(2023)
摘要
The usage of masks during the pandemic has made identifying criminals using surveillance cameras very difficult. Generating the facial features behind a mask is a type of image inpainting. Current research on image inpainting shows promising results on manually pixelated regular holes/patches but has not been designed to handle the specific case of “unmasking” faces. In this paper we propose a novel, custom U-Net based Convolutional Neural Network to regenerate the face under a mask. Simulation results demonstrate that our proposed framework can achieve more than 97% Structural Similarity Index Measure for different types of facial masks across different faces, irrespective of gender, race or color.
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关键词
inpainting, GAN, CNN, U-Net, encoder, decoder
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