# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 1 Introduction
If the focus is the evaluation of biometric systems, the synthesis of mated impressions is the most important ability of a generative model. For that, the standard unconditional GAN architecture should be extended by two mechanisms: conditional generation (taking _fingerprint identity_ as a condition) and the simulation of intra-class variations. The majority of fingerprint verification algorithms use minutiae as the means of fingerprint matching. Hence, the finger's identity is de facto given by co-allocation of minutiae points. Hence, the straightforward idea to generate a fingerprint for a particular finger is to reconstruct it from minutiae. In our previous paper [7], we have demonstrated that the pix2 pix architecture is a powerful and scalable conditional GAN (cGAN) that fits well for reconstructing fingerprint images from minutiae. Reconstruction from minutiae is also a key for fulfilling the requirements on the anonymity and diversity (as these are introduced in [8]) of synthetic fingerprints because the minutiae sets can be created in a pseudo-random way or simply derived from synthetic fingerprints that are known to be anonymous. The _main contribution_ of this paper is in compiling and providing the research community with two datasets of realistic synthetic fingerprints. In order to compile datasets, we combined two concepts: (i) the generation of minutiae sets for mated and non-mated impressions and (ii) the reconstruction of realistic fingerprints from minutiae. The latter concept has been introduced in our previous report [7], while the former concept is the _key novelty_ of this paper. In particular, our _novel_ approach is in combining model-based and data-driven fingerprint generation techniques to create _realistic_ and _anonymous_ fingerprints with an option for _mated_ impressions. The high-level view of our approach for compiling datasets is depicted in Figure 1. The dashed lines refer to the parts introduced in our previous report, while the solid lines and text in bold refer to the new parts firstly reported in this paper. We first define 500 virtual subjects with 8 fingers each. For each finger, we define a basic pattern based on global statistics and generate a non-realistic fingerprint with this basic pattern as a source of minutiae (denoted as non-realistic pseudo-random synthetic fingerprint). After extracting minutiae, we slightly manipulate minutiae locations and orientations as well as remove a small subset of minutiae (denoted as pseudo-random minutiae modification) to create nine further sets of minutiae for generating mated impressions. Next, we synthesize ten impressions per finger, making use of the two selected pix2 pix models from our previous study [7]. In total, each dataset comprises 40,000 samples. The synthetic fingerprints are assessed using the NIST fingerprint image quality (NFIQ) 2 tool for approving their visual quality and Verifinger SDK for measuring the reconstruction success. Subjectively, the fingerprints are almost indistinguishable from real ones. The synthetic datasets are designed to possess characteristics of real biometric datasets, so we believe they can be applied for the privacy-friendly testing of fingerprint recognition systems. Both datasets and generative models are publicly available at [[https://gitti.cs.uni-magdeburg.de/[PERSON]/gensynth-pix2](https://gitti.cs.uni-magdeburg.de/[PERSON]/gensynth-pix2) pix]([https://gitti.cs.uni-magdeburg.de/[PERSON]/gensynth-pix2](https://gitti.cs.uni-magdeburg.de/[PERSON]/gensynth-pix2) pix) (accessed on 4 September 2023). Hereafter, the paper is organized as follows: Section 2 provides an overview of related works. In Section 3, we first elaborate on the generation of pix2 pix models and then show how these models are applied to a compilation of synthetic datasets with mated and non-mated impressions. In Section 4, we first assess the performance of the generative models and then approve the utility of our synthetic datasets. In Section 5, we critically discuss the evaluation results.