Automatic Latent Fingerprint Segmentation

TL;DR

This paper introduces SegFinNet, an effective fully convolutional neural network-based method for automatic latent fingerprint segmentation that improves segmentation accuracy and enhances fingerprint matching performance.

Contribution

The paper presents a novel end-to-end neural network approach for latent fingerprint segmentation that outperforms existing methods and human markup across multiple databases.

Findings

SegFinNet outperforms state-of-the-art latent segmentation algorithms.

Improved segmentation leads to higher fingerprint matching hit rates.

The method is effective across diverse latent fingerprint datasets.

Abstract

We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet. SegFinNet takes a latent image as an input and outputs a binary mask highlighting the friction ridge pattern. Our algorithm combines fully convolutional neural network and detection-based approaches to process the entire input latent image in one shot instead of using latent patches. Experimental results on three different latent databases (i.e. NIST SD27, WVU, and an operational forensic database) show that SegFinNet outperforms both human markup for latents and the state-of-the-art latent segmentation algorithms. We further show that this improved cropping boosts the hit rate of a latent fingerprint matcher.