Synthetic Latent Fingerprint Generator

TL;DR

This paper introduces a CycleGAN-based method to generate synthetic latent fingerprint images with controllable distortions, improving the training data for latent fingerprint recognition and enhancing matcher performance.

Contribution

The work demonstrates the realism of synthetic latents compared to crime scene latents and shows how synthetic data augmentation boosts fingerprint matcher accuracy.

Findings

Synthetic latents are similar to crime scene latents in quality measures.

Data augmentation with synthetic latents nearly doubles Rank-1 retrieval performance.

The approach can enhance various latent fingerprint recognition components.

Abstract

Given a full fingerprint image (rolled or slap), we present CycleGAN models to generate multiple latent impressions of the same identity as the full print. Our models can control the degree of distortion, noise, blurriness and occlusion in the generated latent print images to obtain Good, Bad and Ugly latent image categories as introduced in the NIST SD27 latent database. The contributions of our work are twofold: (i) demonstrate the similarity of synthetically generated latent fingerprint images to crime scene latents in NIST SD27 and MSP databases as evaluated by the NIST NFIQ 2 quality measure and ROC curves obtained by a SOTA fingerprint matcher, and (ii) use of synthetic latents to augment small-size latent training databases in the public domain to improve the performance of DeepPrint, a SOTA fingerprint matcher designed for rolled to rolled fingerprint matching on three latent databases (NIST SD27, NIST SD302, and IIITD-SLF). As an example, with synthetic latent data augmentation, the Rank-1 retrieval performance of DeepPrint is improved from 15.50% to 29.07% on challenging NIST SD27 latent database. Our approach for generating synthetic latent fingerprints can be used to improve the recognition performance of any latent matcher and its individual components (e.g., enhancement, segmentation and feature extraction).