Jisuanji kexue yu tansuo (Oct 2021)

Review of New Face Occlusion Inpainting Technology Research

  • LIU Ying, ZHANG Yixuan, SHE Jianchu, WANG Fuping, LIM Kengpang

Journal volume & issue
Vol. 15, no. 10
pp. 1773 – 1794


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In criminal investigation, if the face image of a suspect is occluded and the face feature points are corrupted, the precise removal of the occluded area becomes an important step in improving face recognition technology. Therefore, face occlusion inpainting has important research significance. The recent progress of face occlusion inpainting technology is described, and based on the deep learning-based image restoration algorithm first proposed in 2016, various face occlusion inpainting fusion algorithms proposed by scholars from 2017 to the present are introduced. Firstly, the existing algorithms are classified into random occlusion and regular occlusion face restoration according to the different ways of occlusion, then further classified into based on convolutional neural network (CNN) and based on generative adversarial network (GAN) according to the different predictive generation networks in the algorithms. This paper analyzes various types of fusion algorithms in terms of model network characteristics, advantages and disadvantages as well as applicable scenarios, and gives some suggestions for the selection of fusion algorithms. The regular occlusion algorithms and random occlusion algorithms are compared and summarized in terms of network structure and applicability range. Then it introduces and summarizes the commonly used image restoration effect evaluation indices and datasets, and by listing the experimental results of various restoration algorithms, it refines and analyzes their quantitative indices and visual effects, and illustrates that the face occlusion inpainting technology has made great progress in recent years. Finally, the future development trend of face occlusion inpainting technology is pointed out from five aspects, such as dataset, algorithm and evaluation index, by combining the existing algorithms and practical requirements.