Latent Fingerprint Enhancement Using Generative Adversarial Networks

Latent fingerprints recognition is very useful in law enforcement and forensics applications. However, automated matching of latent fingerprints with a gallery of live scan images is very challenging due to several compounding factors such as noisy background, poor ridge structure, and overlapping unstructured noise. In order to efficiently match latent fingerprints, an effective enhancement module is a necessity so that it can facilitate correct minutiae extraction. In this research, we propose a Generative Adversarial Network based latent fingerprint enhancement algorithm to enhance the poor quality ridges and predict the ridge information. Experiments on two publicly available datasets, IIITD-MOLF and IIITD-MSLFD show that the proposed enhancement algorithm improves the fingerprints quality while preserving the ridge structure. It helps the standard feature extraction and matching algorithms to boost latent fingerprints matching performance.