Improving Fingerprint Classification Accuracy Using Image Enhancement Deep Learning Models

Fingerprint verification is widely used to verify individuals based on their unique fingerprint patterns. However, existing fingerprint identification systems encounter challenges while dealing with poor-quality images, smudged or marked fingerprints, and variations in finger positioning. These issues are common for latent fingerprints. This work proposes to address these issues through a novel machine-learning model employing advanced image enhancement techniques. The model aims to enhance fingerprint image quality and minimize the impact of damage using machine learning, ultimately reducing the error rate in identification systems. Specifically, this work proposes to incorporate deep learning image enhancement techniques into the low-quality and latent fingerprints before passing them through another deep learning model to perform verification tasks. This work also provides insights as different experiments are made while applying different approaches to different real-life datasets.