# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 3 Research Methodology
### Compilation of Synthetic Datasets
This operation is covered by the first two blocks in Figure 1.  
Next, we extract minutiae information for all fingerprints using Verifinger SDK [50]. Since captured fingerprints may not be perfectly aligned or positioned, meaning a fingerprint could be shifted from the image center, slightly rotated, or only partially presented during the sensor capture, we simulate the intra-class variations by creating an additional nine impressions for each virtual finger. This operation is called \"pseudo-random modification\" in Figure 1.  
The first fingerprint impression is always the one generated from minutiae extracted from the original Anguli pattern. The other nine impressions are generated from minutiae that undergo random affine transformations or minutiae side cuts. To be more precise, minutiae modifications include:  
* Random rotation varying from \(-\)20 to +20 degrees with a step of 1 degree
* Random shift varying from \(-\)20 to 20 pixels regarding x-axis and y-axis
* Random cut of 0% to 15% of minutiae points at one of eight sides: top-left, top, top-right, left, right, bottom-left, bottom, and bottom-right  
Note that the transformations are applied not to minutiae maps but directly to minutiae coordinates and orientations in a minutiae template. The addition of mated fingerprints expands the dataset of synthetic fingerprints from 4000 samples to **40**,**000 samples. Note that both minutiae maps for original Anguli patterns as well as minutiae maps for mated impressions are generated very quickly. The same applies for the reconstruction of realistic fingerprints from minutiae maps. This means that the number of fingerprints in a synthetic dataset can be significantly up-scaled in a reasonable amount of time. Our synthetic datasets of 40,000 samples can be seen as an example that demonstrates the validity of the proposed technique for the compilation of large-scale synthetic datasets. Our first synthetic dataset is created by applying the **\"aug39k_DL_BN_60+60 ep\"** model to the aforementioned set of 40,000 minutiae templates. Minutiae templates are converted to minutiae maps using \"directed line\" encoding. The dataset is further referred to as **AMSL SynFP P2P v1**. Our second synthetic dataset is created by applying the **\"aug39k_PM_IN_15 ep\"** model to the aforementioned set of 40,000 minutiae templates. Minutiae templates are converted to minutiae maps using \"pointing minutiae\" encoding. The dataset is further referred to as **AMSL SynFP P2P v2**. The last processing step is cutting the central region of 300 \(\times\) 420 pixels from the generated 512 \(\times\) 512 pixel images. In doing so, the irrelevant border regions are dropped, but the resulting images still contain full fingerprints. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Basic Pattern** & **Ulnar Loop** & **Radial Loop** & **Whorl** & **Arch** & **Tented Arch** \\  & **(UL)** & **(RL)** & **(WH)** & **(AR)** & **(TA)** \\ \hline Absolute \# & 44 \(\times\) 50 = 2200 & 4 \(\times\) 50 = 200 & 16 \(\times\) 50 = 800 & 8 \(\times\) 50 = 400 & 8 \(\times\) 50 = 400 \\ \hline Relative \# & 55\% & 5\% & 20\% & 10\% & 10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of basic patterns in our synthetic datasets. The fingerprints in both datasets are anonymous by design. The design of our generative models ensures the realistic appearance of fingerprint patterns, or to be more precise their resemblance with real plain fingerprints captured by a CrossMatch Verifier 300 sensor. The diversity of inter-class samples is ensured by the design of the Anguli tool, and the sufficient variability and non-excessive diversity of intra-class samples will be confirmed in Section 4.2.