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Face Image Reflection Removal

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Abstract

Face images captured through glass are usually contaminated by reflections. The low-transmitted reflections make the reflection removal more challenging than for general scenes because important facial features would be completely occluded. In this paper, we propose and solve the face image reflection removal problem. We recover the important facial structures by incorporating inpainting ideas into a guided reflection removal framework, which takes two images as the input and considers various face-specific priors. We use a newly collected face reflection image dataset to train our model and compare with state-of-the-art methods. The proposed method shows advantages in estimating reflection-free face images for improving face recognition.

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Correspondence to Renjie Wan or Boxin Shi.

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Communicated by Yoichi Sato.

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The research work was done at the Rapid-Rich Object Search (ROSE) Lab, Nanyang Technological University. The work is supported in part by the Wallenberg-NTU Presidential Postdoctoral Fellowship, the NTU-PKU Joint Research Institute, a collaboration between the Nanyang Technological University and Peking University that is sponsored by a donation from the Ng Teng Fong Charitable Foundation, and the Science and Technology Foundation of Guangzhou Huangpu Development District under Grant 201902010028. This research is in part supported by the National Natural Science Foundation of China under Grants 61872012 and U1611461, and Beijing Academy of Artificial Intelligence (BAAI).

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Wan, R., Shi, B., Li, H. et al. Face Image Reflection Removal. Int J Comput Vis 129, 385–399 (2021). https://doi.org/10.1007/s11263-020-01372-5

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