Review on Advancements in Fingerprint Analysis: From Historical Developments to Modern Deep Learning Applications

The evolution of fingerprint analysis traces its roots from the early studies by Marcello Malpighi in the 17th century to its crucial role in modern forensic science. Over time, significant advancements in technology, particularly in image processing and machine learning, have revolutionized fingerprint analysis. This paper explores the historical development of fingerprint analysis, the importance of digital processing, and the role of Automated Fingerprint Recognition Systems (AFRS). It further delves into the significance of noise removal and image-denoising techniques, highlighting the impact of state-of-the-art methods such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The integration of deep learning techniques has significantly enhanced fingerprint-matching accuracy, speed, and reliability, marking a pivotal shift in forensic investigations and biometric authentication.