# Deep Learning in the Field of Biometric Template Protection: An Overview

arXiv: https://arxiv.org/abs/2303.02715 · HTML: https://mlsanity.com/paper/2303.02715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 81 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [On the Reconstruction of Face Images from Deep Face Templates](https://mlsanity.com/paper/1703.00832/) (2017)
- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [From BoW to CNN: Two Decades of Texture Representation for Texture Classification](https://mlsanity.com/paper/1801.10324/) (2018)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images](https://mlsanity.com/paper/1712.00321/) (2017)
- [An Overview of Privacy-enhancing Technologies in Biometric Recognition](https://mlsanity.com/paper/2206.10465/) (2022)
- [Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems](https://mlsanity.com/paper/2006.08376/) (2020)
- [Deep Face Fuzzy Vault: Implementation and Performance](https://mlsanity.com/paper/2102.02458/) (2021)
- [Deep Hashing for Secure Multimodal Biometrics](https://mlsanity.com/paper/2012.14758/) (2020)
- [Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques](https://mlsanity.com/paper/2110.05044/) (2021)
- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [An Attack on Facial Soft-biometric Privacy Enhancement](https://mlsanity.com/paper/2111.12405/) (2021)

## Built on by (most cited first, 1 of 1)

- [Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing](https://mlsanity.com/paper/2310.02997/) (2023)

## Abstract (arXiv)

Today, deep learning represents the most popular and successful form of machine learning. Deep learning has revolutionised the field of pattern recognition, including biometric recognition. Biometric systems utilising deep learning have been shown to achieve auspicious recognition accuracy, surpassing human performance. Apart from said breakthrough advances in terms of biometric performance, the use of deep learning was reported to impact different covariates of biometrics such as algorithmic fairness, vulnerability to attacks, or template protection. Technologies of biometric template protection are designed to enable a secure and privacy-preserving deployment of biometrics. In the recent past, deep learning techniques have been frequently applied in biometric template protection systems for various purposes. This work provides an overview of how advances in deep learning take influence on the field of biometric template protection. The interrelation between improved biometric performance rates and security in biometric template protection is elaborated. Further, the use of deep learning for obtaining feature representations that are suitable for biometric template protection is discussed. Novel methods that apply deep learning to achieve various goals of biometric template protection are surveyed along with deep learning-based attacks.