Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition
This addresses a specific issue for face recognition systems by mitigating the negative impact of facial tattoos, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of facial tattoos degrading face recognition systems by proposing a generator for adding realistic tattoos to facial images and a deep learning model for tattoo removal, showing that tattoo removal can improve face recognition accuracy without degrading image quality.
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.