# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 1 Introduction  
The range of applications that make use of biometric user authentication based on fingerprints is very broad. On the one end, there are uncritical applications, e.g., mobile phones can be unlocked by scanning a fingertip. On the other hand, there are very sensitive access-control systems that scan fingers to grant access to bank accounts or governmental services. The recent trend of developing fingerprint processing and recognition algorithms is an application of deep learning, or to be more precise, training of deep convolutional neural networks (DCNN). In fact, deep learning-based approaches recently outperformed almost all traditional approaches, even in the domain of fingerprint processing. However, the training of DCNN models is data-greedy, meaning that a huge number of samples is needed. This makes collecting a large-scale dataset of biometric samples a prerequisite for the development of up-to-date fingerprint recognition systems. Considering interpretability and explainability issues related to DCNN, training networks with real biometric data raises serious privacy concerns because it is hard or almostimpossible to ensure that no training samples can be induced from the outcomes of a trained DCNN model [1]. Hence, training datasets need to be privacy-friendly, meaning that they are harmonized with local regulations on the protection of private data. It is important to note that in the European Union, biometric data is a subject of protection by the General Data Protection Regulation (GDPR) and in covered by Article 9. Even if this article might not apply to using real biometric datasets in academic research, biometric data are considered to be a special case of private data, which means that there are strong restrictions on collecting, processing, and sharing this type of data. All in all, the requirements on private data protection make the usage of real biometric data inconvenient or in some cases even impossible. The eventual conflicts with local data protection regulations forced many institutions to withdraw their biometric datasets from public access. For instance, the National Institute of Standards and Technology (NIST) has withdrawn the fingerprint datasets SD4, SD14, and SD27. Instead, the fingerprint dataset SD300 has been made publicly available, for which the Federal Bureau of Investigation (FBI) explicitly confirmed that all subjects presented in the dataset are deceased, see [[https://www.nist.gov/itl/iad/imagegroup/nist-special-database-300](https://www.nist.gov/itl/iad/imagegroup/nist-special-database-300)]([https://www.nist.gov/itl/iad/imagegroup/nist-special-database-300](https://www.nist.gov/itl/iad/imagegroup/nist-special-database-300)) (accessed on 4 September 2023). A simple and inexpensive way to get around privacy concerns related to real fingerprints is to introduce virtual individuals along with their synthetic fingerprints. It is important that synthetic fingerprints look like real fingerprints, which implies that they appear realistic to the human eye and that the generated images possess the same statistical characteristics as real fingerprint images. Moreover, synthetic fingerprints should be anonymous, which means that it is impossible to link a synthetic fingerprint to a finger of any natural person that is presented in the dataset used for training of the fingerprint generator. With the recent development of generative adversarial networks (GAN), the task of realistic image synthesis can be considered solved. As demonstrated in [2; 3], the synthesis of _random_ fingerprint images that appear realistic to the human eye and possess visual characteristics of fingerprints used for generative model training can be conducted by any advanced NVIDIA GAN architecture such as progressive growing GAN (PGGAN) [4], StyleGAN [5], or StylaGAN2 [6]. However, these architectures neither ensure anonymity nor are capable of generating mated impressions (different fingerprints of the same finger).