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FSA-Net: A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization

Zhipeng Bin1, Huihuang Zhao1,2,*, Xiaoman Liang1,2, Wenli Chen1

1 College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China
2 Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China

* Corresponding Author: Huihuang Zhao. Email: email

Intelligent Automation & Soft Computing 2023, 37(1), 971-983. https://doi.org/10.32604/iasc.2023.037270

Abstract

The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images. In this study, the Face Swapping Attention Network (FSA-Net) is proposed to generate photorealistic face swapping. The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint (cheek, mouth, eye, nose, etc.), which causes artifacts and makes the generated face silhouette non-realistic. To address this problem, a novel reinforced multi-aware attention module, referred to as RMAA, is proposed for handling facial fusion and expression occlusion flaws. The framework includes two stages. In the first stage, a novel attribute encoder is proposed to extract multiple levels of target face attributes and integrate identities and attributes when synthesizing swapped faces. In the second stage, a novel Stochastic Error Refinement (SRE) module is designed to solve the problem of facial occlusion, which is used to repair occlusion regions in a semi-supervised way without any post-processing. The proposed method is then compared with the current state-of-the-art methods. The obtained results demonstrate the qualitative and quantitative outperformance of the proposed method. More details are provided at the footnote link and at .

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APA Style
Bin, Z., Zhao, H., Liang, X., Chen, W. (2023). Fsa-net: A cost-efficient face swapping attention network with occlusion-aware normalization. Intelligent Automation & Soft Computing, 37(1), 971-983. https://doi.org/10.32604/iasc.2023.037270
Vancouver Style
Bin Z, Zhao H, Liang X, Chen W. Fsa-net: A cost-efficient face swapping attention network with occlusion-aware normalization. Intell Automat Soft Comput . 2023;37(1):971-983 https://doi.org/10.32604/iasc.2023.037270
IEEE Style
Z. Bin, H. Zhao, X. Liang, and W. Chen "FSA-Net: A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization," Intell. Automat. Soft Comput. , vol. 37, no. 1, pp. 971-983. 2023. https://doi.org/10.32604/iasc.2023.037270



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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