Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition

TL;DR

This paper introduces a fusion-based local matching method combining handcrafted minutiae features and deep neural network embeddings to enhance latent fingerprint recognition accuracy, especially under challenging conditions.

Contribution

It proposes a multi-stage fusion approach that integrates minutiae cylinder codes with neural embeddings, improving recognition performance over existing methods.

Findings

Significant increase in rank-1 accuracy on public and private datasets

Outperforms state-of-the-art methods in latent fingerprint recognition

Effective in handling deformations and artifacts in latent images

Abstract

Latent fingerprints are one of the most widely used forensic evidence by law enforcement agencies. However, latent recognition performance is far from the exemplary performance of sensor fingerprint recognition due to deformations and artifacts within these images. In this study, we propose a fusion based local matching approach towards latent fingerprint recognition. Recent latent recognition studies typically relied on local descriptor generation methods, in which either handcrafted minutiae features or deep neural network features are extracted around a minutia of interest, in the latent recognition process. Proposed approach would integrate these handcrafted features with a recently proposed deep neural network embedding features in a multi-stage fusion approach to significantly improve latent recognition results. Effectiveness of the proposed approach has been shown on several public and private data sets. As demonstrated in our experimental results, proposed method improves rank-1 identification accuracy by considerably for real-world datasets when compared to either the single usage of these features or existing state-of-the-art methods in the literature.