论文标题
墨水下方面对：综合数据和纹身去除，并应用于面部识别
Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition
论文作者
论文摘要
近年来，分析面孔的系统已经有了显着改善，如今已在许多应用方案中使用。但是，已经发现这些系统受到纹身等面部改变的负面影响。为了更好地理解和减轻面部分析系统中面部纹身的影响，需要和没有纹身的个体图像的大量数据集。为此，我们提出了一个生成器，以自动将逼真的纹身添加到面部图像中。此外，我们通过使用基于深度学习的模型从面部图像中删除纹身来证明一代的可行性。实验结果表明，可以从真实图像中去除面部纹身而不会降低图像质量。此外，我们表明，在提取和比较面部特征之前，使用提出的基于基于学习的深度纹身去除拟议的深度学习纹身来提高面部识别精度。
Systems that analyse faces have seen significant improvements in recent years and are today used in numerous application scenarios. However, these systems have been found to be negatively affected by facial alterations such as tattoos. To better understand and mitigate the effect of facial tattoos in facial analysis systems, large datasets of images of individuals with and without tattoos are needed. To this end, we propose a generator for automatically adding realistic tattoos to facial images. Moreover, we demonstrate the feasibility of the generation by using a deep learning-based model for removing tattoos from face images. The experimental results show that it is possible to remove facial tattoos from real images without degrading the quality of the image. Additionally, we show that it is possible to improve face recognition accuracy by using the proposed deep learning-based tattoo removal before extracting and comparing facial features.
