  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.
