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.
翻译:分析面部的系统近年来有了显著的改进,如今用于许多应用方案。然而,这些系统却因面部改变(如纹身)而受到负面影响。为了更好地了解和减轻面部分析系统中面部纹身的影响,需要大量关于有纹身和没有纹身的个人图像的数据集。为此,我们提议在面部图像中自动添加切合实际的纹身。此外,我们通过使用深层学习模型将纹身从面部图像中去除来证明生成的可行性。实验结果显示,在提取和比较面部特征之前,可以使用深层学习的纹身去除法,从而提高面部纹身的准确性。此外,我们表明,在提取和比较面部特征之前,通过使用拟议的深层学习的纹身去除法,可以提高面部纹身的准确性。