# Generative Convolutional Networks for Latent Fingerprint Reconstruction

## TL;DR

This paper introduces a generative convolutional network approach to enhance fingerprint images by denoising and reconstructing missing ridge patterns, improving the accuracy of fingerprint recognition especially in corrupted or partial images.

## Contribution

It presents a novel application of generative convolutional networks for latent fingerprint enhancement, combining deep learning with traditional feature extraction methods.

## Key findings

- Improved ridge pattern reconstruction in corrupted fingerprint images.
- Enhanced fingerprint recognition accuracy on multiple datasets.
- Effective pre-processing step for latent fingerprint recognition systems.

## Abstract

Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step that noticeably reduces false positive and negative detection rates. A particularly challenging setting is when the fingerprint images are corrupted or partially missing. In this work, we apply generative convolutional networks to denoise visible minutiae and predict the missing parts of the ridge pattern. The proposed enhancement approach is tested as a pre-processing step in combination with several standard feature extraction methods such as MINDTCT, followed by biometric comparison using MCC and BOZORTH3. We evaluate our method on several publicly available latent fingerprint datasets captured using different sensors.