Impact of Facial Tattoos and Paintings on Face Recognition Systems
Not on the map. Why?
Abstract (arXiv)
In the past years, face recognition technologies have shown impressive recognition performance, mainly due to recent developments in deep convolutional neural networks. Notwithstanding those improvements, several challenges which affect the performance of face recognition systems remain. In this work, we investigate the impact that facial tattoos and paintings have on current face recognition systems. To this end, we first collected an appropriate database containing image-pairs of individuals with and without facial tattoos or paintings. The assembled database was used to evaluate how facial tattoos and paintings affect the detection, quality estimation, as well as the feature extraction and comparison modules of a face recognition system. The impact on these modules was evaluated using state-of-the-art open-source and commercial systems. The obtained results show that facial tattoos and paintings affect all the tested modules, especially for images where a large area of the face is covered with tattoos or paintings. Our work is an initial case-study and indicates a need to design algorithms which are robust to the visual changes caused by facial tattoos and paintings.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 61 references that are in the map. First posted on arXiv 17 Mar 2021.
Builds on
19 of 61 references.
References · 19
- 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,095
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 895
- 2018Deep Face Recognition: A Surveycited by 275
- 2020DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detectioncited by 231
- 2018DeepFakes: a New Threat to Face Recognition? Assessment and Detectioncited by 208
- 2020Media Forensics and DeepFakes: an overviewcited by 208
- 2019STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editingcited by 119
- 2020SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustnesscited by 111
- 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
- 2020Face Image Quality Assessment: A Literature Surveycited by 79
- 2016Automatic Face Reenactmentcited by 43
- 2020Biometric Quality: Review and Application to Face Recognition with FaceQnetcited by 41
7 more
- 2020A survey of face recognition techniques under occlusioncited by 38
- 2019Decorrelated Adversarial Learning for Age-Invariant Face Recognitioncited by 21
- 2015Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?cited by 10
- 2017Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Losscited by 9
- 2017Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencodercited by 8
- 2018Recognizing Disguised Faces in the Wildcited by 7
- 2020Detection of Makeup Presentation Attacks based on Deep Face Representationscited by 4
Built on by
Citing papers per year: 2022: 2.
2 papers.