# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 1 Introduction  
The use of fingerprints as a biometric identifier for person recognition is considered robust and reliable due to their unique, unalterable, and enduring characteristics over time [1]. While other biometric techniques share similar attributes--such as facial and iris recognition for identification of individuals or palm print identification [2] for classification of age and gender--fingerprints stand out as more accessible and cost-effective, particularly regarding scanning technology [3]. Two primary applications of person recognition can be identified. The first straightforward and expedient method involves validating whether an individual is who they claim to be, a process commonly referred to as identity verification. In this scenario, a fingerprint is compared to the corresponding record stored in a database (DB) using a one-to-one matching procedure. The second application, which is the primary focus of this study, addresses situations where an individual's identity is unknown. In such cases, the fingerprint must be matched against the entire DB, requiring a one-to-many comparison process. Recent advances in fingerprint localization have focused on improving accuracy through the use of novel similarity metrics [4]. Due to the constant growth of the human population, large fingerprint databases are continuously generated, making it increasingly complex for identification systems to provide instant and precise results [5]. To improve the response times of these identification systems, a preprocessing step--fingerprint classification--is employed to reduce the global search space. Addressing this challenge, convolutional neural networks (CNNs) achieve 100% accuracy but at the expense of excessive computational costs [6; 7]. Techniques such as the use of an orientation field (OF), support vector machines (SVMs), and random forests (RFs) have been reported to yield satisfactory results. In [6], a threshold above 1 resulted in 100% accuracy, while a threshold of 0.6 achieved over 93% accuracy using the National Institute of Standards and Technology Special Database 4 (NIST-4), which contains 4000 samples. Nevertheless, this excellent performance was impacted by the high structural similarity of ridges in certain samples. As the threshold increases, classification confusion also rises [6]. Despite the high accuracy achieved by CNNs in fingerprint classification, their application in real-world scenarios is often impractical due to their high computational demands and extensive training requirements [8]. In situations where decisions must be made in real time or within strict time constraints, such as in medical service provision or border control systems, the inference time of CNNs can become a critical bottleneck. These applications require rapid processing and reliable identification without depending on specialized high-performance hardware. Consequently, alternative models that balance accuracy and computational efficiency are necessary to ensure practical implementation in large-scale fingerprint identification systems [9]. The authors of [7] evaluated SVM and RF performance using several databases, including DB-HLG, the fingerprint verification competition (FVC) datasets (2000, 2002, and 2004), and NIST-4. The SVM achieved an accuracy of \(\geq\)95.5% and a mean squared error (MSE) of \(\leq\)0.321, with computational times of 7, 9, and 5 h for each database, respectively. The RF obtained an accuracy of \(\geq\)96.75%; an MSE of \(\leq\)0.274; and computational times of 8, 12, and 18 h for the same databases. As evidenced, machine learning offers significant advantages in fingerprint classification. However, these networks require high-performance computational architectures, which often render their application impractical. To address these limitations, this work proposes the use of a multilayer extreme learning machine (M-ELM) for efficient fingerprint classification, with the primary objective of improving training times and computational costs.