# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 2 State of the Art
### ELM with Descriptors  
In [13], a novel fingerprint classification approach based on feature extraction models and both basic and modified ELMs was proposed, marking the first time this approach was adopted. In general, ELMs produce good results when dealing with imbalanced data, as in this case, where fingerprint types were distributed across five imbalanced classes. The descriptors used in [10], Capelli02 [24], Hong08 [14], and Liu10 [25], are based on the most relevant visual characteristics of fingerprints. Given the imbalanced classes in fingerprint identification schemes, the ELM was optimized (standard, weighted, and decay-weighted) in terms of geometric mean, estimating its hyperparameters (regularization parameter, number of hidden neurons, and disintegration parameter). SFinGe software (v.4.1.1746) was used to generate the dataset, creating synthetic fingerprints with realistic appearances, varying quality levels (translations, rotations, and geometric deformations), and true class labels. To emulate different scenarios, three quality profiles were used to generate fingerprints: High Quality No Perturbations (HQNoPert), Default, and Varying Quality and Perturbations (VQandPert). The HQNoPert database consists of high-quality fingerprints without perturbations. The default database contains medium-quality fingerprints with slight localization and rotational disturbances. The VQAndPert database includes fingerprints of varying quality with location, rotation, and geometric disturbances. The quality of the images is the only difference among the databases. A total of 30,000 fingerprints were generated, with 10,000 for each quality level. Classic metrics such as precision and rate of penetration were calculated for comparison with superior CNN-based methods reported in the literature. Experimental results show that the weighted ELM 2 (W-ELM2) with the golden ratio in the weighted matrix generally outperforms other ELMs (with 0.95% accuracy and a 0.0332% penetration rate). The combination of the Hong08 extractor and W-ELM2 competes with CNNs in terms of fingerprint classification efficacy, but the ELM-based methods demonstrate faster training speeds in all contexts. In [34], a fingerprint classification system utilizing the ELM algorithm was presented, employing a heuristic approach to maximize classification precision. This optimization was achieved by determining the optimal parameters--the activation mapping function and number of hidden neurons--that yield the best results. The optimal configuration attained a classification accuracy of 95.5%, using the bounded sign mapping function, the SFinGe ACSP quality database, the Hong08 [14] descriptor, and 1832 hidden nodes. The evaluation was performed using the NIST-4 and SFinGe databases under the following conditions. (1) NIST-4: This dataset comprises 4000 fingerprint images, each with dimensions of 512 \(\times\) 480 pixels. Each fingerprint was scanned twice at a resolution of 500 DPI. The fingerprints are distributed naturally across five classes, but due to class similarities, 350 fingerprints are labeled with dual-class identifiers. Consistent with prior studies [3; 5; 10; 23; 35], fingerprints with dual-class labels were excluded, resulting in a final dataset of 1650 fingerprints (3300 images in total). (2) SFinGe: Following the methodology outlined in previous works [3; 10], SFinGe software was used to generate three datasets of synthetic fingerprints with varying image quality and natural class distribution. The software parameters included an acquisition area of 14.6 \(\times\) 19.6 mm\({}^{2}\), a resolution of 500 DPI, and an image size of 288 \(\times\) 384 pixels. The three generated quality levels were: high quality undisturbed (HQU), default (D), and quality with variant disturbance (QVD). Each of these datasets contains 120,000 samples.