# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 4 Methods and Materials  
To evaluate performance, the 5-FCV scheme is used [49], ensuring unbiased and precise classification metrics [10; 13]. This is because the validation and training sets are not static but come from five parts the DB. Thus, 20% is taken for validation, while the remaining four parts are used for training. For each database, feature descriptor, and ELM approach, the overall results are reported from an average of five runs. Evidently, the validation set is intended to find the ELM hyperparameters that maximize its classification ability, and only the number of hidden neurons (\(N\)) is considered for heuristic optimization for simplicity and performance purposes (see next section). Accuracy and absolute error of the PR. Accuracy is chosen, as it is a widely accepted metric for artificial intelligence models and aligns with previous studies used for comparison [10; 13; 29; 50; 51]. PR, on the other hand, is particularly relevant for addressing unbalanced datasets, making it a standard metric in fingerprint analysis [10; 13]. The absolute value of PR allows for straightforward identification of the superior model, with values approaching zero indicating a better penetration rate. Given the naturally unbalanced distribution of the database, the target PR for this study is 0.2948 [13].  
In this study, SFINGE software [52] was utilized to facilitate comparisons with significant and recent works reported in the state of the art [10; 13]. In addition, this software generates a substantial database consisting of thousands of fingerprint samples, enabling conclusive observations regarding fingerprint-based individual classification [10; 13; 51]. SFINGE software produces synthetic fingerprints of different quality levels (low, medium, and high) with a realistic class distribution (unbalanced classes). These synthetic fingerprints generated by SFINGE simulate fingerprints obtained by optical scanners, which are much closer to real systems, such as NIST-4 [53]. Moreover, it is worth noting that the feasibility of adopting the SFINGE database has already been demonstrated in several editions of the FVC [54; 55; 56; 57; 58], where it achieved results comparable to those of real fingerprint databases, following the natural class distribution. To emulate various scenarios, three quality profiles were generated using SFINGE: HQNoPert, Default, and VQandPert (see Figure 4). The HQNoPert database consists of high-quality, undisturbed fingerprints. The default database includes fingerprints of medium quality and slight perturbationsin localization and rotation. The VQAndPert database comprises fingerprint of varying quality levels, incorporating perturbations related to the location, rotation, and geometric distortions. The primary distinction among the databases lies in the quality of the generated images. In total, 10,000 fingerprints were generated for each quality category, resulting in a comprehensive dataset of 30,000 fingerprints. The generation of 10,000 fingerprints per category ensures a fair and consistent basis for comparison with the studies reported in the state of the art [10,13].