# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 2 Background and Related Work
### Data-Driven Fingerprint Generation
#### 2.2.2 Fingerprint Generation via GANs  
According to [25], GANs outperform statistical modeling at generating fingerprint images by capturing their underlying probability distribution and replicating the overall appearance from training samples, rather than modeling fingerprint attributes. Although GANs enhance the visual quality of generated samples, they fall short of accurately capturing the crucial fingerprint characteristics due to their reliance on random inputs during synthesis.  
Finger-GAN proposed in [26] is the very first study that utilizes a GAN-based framework to create synthetic fingerprint images. The proposed approach uses the deep convolutional generative adversarial network (DCGAN) [20] with a modified loss function. The primary goal is that the lines in created fingerprint images are connected, just like they are in real fingerprints. The network is trained based on two fingerprint databases fingerprint verification competition (FVC) 2006 [27] and The Hong Kong Polytechnic University (PolyU) [28], making use of data augmentation. The performance is validated using the Frechet inception distance (FID) [29].  
Research in [30] proposes a GAN model with loss-doping, which helps prevent a mode collapse and improves convergence. The generator incorporates residual connections, enhancing stability throughout the training process. Data augmentation techniques are employed. Due to hardware limitations, the synthesized fingerprints are limited to the size of 256 \(\times\) 256 pixels. However, it is stated that the concept can be extended to synthesize bigger images.  
In [2,3], it is demonstrated how the common NVIDIA GAN architectures such as PGGAN, StyleGAN, or StyleGAN2 can be applied to the random generation of partial and full realistic fingerprints.  
The study in [31] proposes an approach of synthesizing high-resolution fingerprints of up to 512 \(\times\) 512 pixels using the improved Wasserstein GAN (IWGAN) [32] and a convolutional autoencoder. The methodology consists of two phases: training the convolutional autoencoder and then training the IWGAN. The autoencoder includes an encoder to embed the input fingerprint and a decoder to reproduce the original fingerprint from the embedded vector. The IWGAN is trained with the generator initialized using the decoder part of the autoencoder. The fingerprints generated in this way lack uniqueness. This issue is addressed in [33] by incorporating the identity loss, guiding the generator to synthesize fingerprints corresponding to more distinct identities.  
In [34], it is proposed to combine traditional statistical modeling with CycleGAN to create master-fingerprints with level three features (sweat pores) and make them appear realistic by performing style transfer with the image-to-image translation. This approach allows for producing several instances of one finger.  
The most recent approach utilizing GANs for fingerprint synthesis is PrintsGAN [17]. The synthesized samples closely replicate the minutiae quantity, type, and quality distributions observed in target fingerprint datasets. The proposed method gives increased control over the synthesis process and enhances the realism of rolled fingerprints in high-resolution images with a size of 512 \(\times\) 512 pixels.