Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition

TL;DR

This paper introduces a deep learning approach that generates realistic facial tattoos and removes them, enhancing face recognition accuracy by mitigating tattoo-related alterations in facial images.

Contribution

It presents a novel generator for realistic tattoo synthesis and a deep learning-based method for tattoo removal, improving face recognition performance.

Findings

Tattoo removal maintains image quality.

Tattoo removal improves face recognition accuracy.

Generated tattoos are realistic and useful for training.

Abstract

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.