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

arXiv: https://arxiv.org/abs/2202.05297 · HTML: https://mlsanity.com/paper/2202.05297/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 58 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 20 of 20)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network](https://mlsanity.com/paper/1803.07835/) (2018)
- [Generative Face Completion](https://mlsanity.com/paper/1704.05838/) (2017)
- [A survey of face recognition techniques under occlusion](https://mlsanity.com/paper/2006.11366/) (2020)
- [Geometry-Aware Face Completion and Editing](https://mlsanity.com/paper/1809.02967/) (2018)
- [Level Three Synthetic Fingerprint Generation](https://mlsanity.com/paper/2002.03809/) (2020)
- [SynCoLFinGer: Synthetic Contactless Fingerprint Generator](https://mlsanity.com/paper/2110.09144/) (2021)
- [Identity Preserving Face Completion for Large Ocular Region Occlusion](https://mlsanity.com/paper/1807.08772/) (2018)
- [Does Generative Face Completion Help Face Recognition?](https://mlsanity.com/paper/1906.02858/) (2019)
- [Recognizing Disguised Faces in the Wild](https://mlsanity.com/paper/1811.08837/) (2018)
- [Impact of Facial Tattoos and Paintings on Face Recognition Systems](https://mlsanity.com/paper/2103.09939/) (2021)

## Built on by (most cited first, 2 of 2)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [TattTRN: Template Reconstruction Network for Tattoo Retrieval](https://mlsanity.com/paper/2405.07571/) (2024)

## Abstract (arXiv)

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.