# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 2 Background and Related Work
### Fingerprint Reconstruction from Minutiae  
Reconstructing fingerprints from the minutiae templates or the inversion of fingerprint templates was initially addressed in 2001 [35]. However, intensive research in this area started in 2007 by [36; 37]. Regarding the model-based approaches, the process is technically similar to fingerprint synthesis with the only difference being that singularity points and the orientation map are estimated from minutiae and not vice versa. The most common approach in [36] estimates the orientation map using a modified model from [38] and applies Gabor filtering iteratively, starting from minutiae locations. The final rendering step enhances realism. In [37], a fingerprint image is generated from a skeleton image, which is reconstructed from minutiae. The process involves estimating an orientation map using minutiae triplets and drawing streamlines starting from minutiae and border points. In [39], a unique method is introduced where a phase image is reconstructed from the minutiae template and subsequently converted into a fingerprint image. In [40], an alternative approach is presented, wherein ridge patterns are reconstructed using patch dictionaries. This method enables the synthesis of idealistic ridge patterns that clearly lack realism. Relying on the assertion that real fingerprints exhibit non-random distributions of minutiae, with different fingerprint types showcasing distinct patterns, the authors in [25] propose an integrated framework that combines fingerprint reconstruction and synthesis. They begin with StyleGAN2 training from the NIST SD14 database [41] to facilitate fingerprint synthesis with the generator part of the model. Then, DCNN (referred to as the minutiae-to-vector encoder) is trained to produce embeddings that act as an input for the GAN generator. By steering latent vectors, visual characteristics of fingerprints such as dry skin artifacts can be controlled. The study in [42] proposes approaches for fingerprint reconstruction from minutiae and from deep neural network embeddings and compares them to each other. The effectiveness of inversion attacks is evaluated, and it is suggested that reconstruction from embeddings is less successful than from minutiae. The application of pix2 pix to fingerprint reconstruction from minutiae was first introduced in [43]. The original challenge is transformed into an image-to-image translation task. First, minutiae points are depicted on a minutiae map, an image visually representing all essential minutiae information. Then, a minutiae map is translated to a fingerprint image. Research in [7; 44] advances the concept of [43] and demonstrates state-of-the-art results in fingerprint reconstruction. A new minutiae encoding approach is introduced, and it is shown in cross-sensor and cross-dataset experiments that the pix2 pix models have potential to generalize [44] effectively. However, the original pix2 pix [24] can only handle images of 256 \(\times\) 256 pixels and lower. In [45], pix2 pixHD is proposed to handle larger images. Our previous study in [7] introduces a simple yet effective method to handle 512 \(\times\) 512 pixel images. In contrast to [45], we extend both the discriminator and generator of the original pix2 pix architecture by adding one convolutional layer. We also proposed an alternative minutiae encoding approach and trained several generative models based on real and synthetic fingerprint data. The work in [7] is an important component of the work presented in this paper enabling generation of large-scale datasets of mated and non-mated fingerprints. Several aforementioned fingerprint synthesis techniques were applied to the compilation of synthetic datasets. However, only a few of them comprise a large amount of realistic full fingerprints at their native resolution of 500 ppi and also contain mated impressions. The most widespread fingerprint datasets used in different years for the fingerprint verification competition (FVC2000 [46], FVC2002 [47], and FVC2004 [48]) contain only 880 synthetic samples each.