Improving Automated Latent Fingerprint Identification using Extended Minutia Types

TL;DR

This paper introduces a novel method that leverages rare and unusual fingerprint minutiae features to enhance the accuracy of automated latent fingerprint identification systems, especially with partial fingerprints.

Contribution

It proposes a new approach that incorporates extended minutiae types into existing matchers, significantly improving rank identification accuracy in forensic fingerprint analysis.

Findings

Significant accuracy improvements with augmented matchers

Effective use of rare minutiae features in forensic cases

Compatibility with multiple existing minutiae-based matchers

Abstract

Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features.