Learning an Ensemble of Deep Fingerprint Representations

TL;DR

This paper introduces an ensemble approach for fingerprint representation learning using multiple DeepPrint models trained on various transformations, combined with a feature fusion technique to improve accuracy without extra computational cost.

Contribution

It proposes a novel method to extract diverse fingerprint embeddings and fuse them into a single, comprehensive representation, enhancing recognition performance.

Findings

Consistent accuracy improvements across five fingerprint databases.

Effective ensemble method that does not increase computational complexity.

Versatile wrapper that enhances any DNN-based fingerprint recognition system.

Abstract

Deep neural networks (DNNs) have shown incredible promise in learning fixed-length representations from fingerprints. Since the representation learning is often focused on capturing specific prior knowledge (e.g., minutiae), there is no universal representation that comprehensively encapsulates all the discriminatory information available in a fingerprint. While learning an ensemble of representations can mitigate this problem, two critical challenges need to be addressed: (i) How to extract multiple diverse representations from the same fingerprint image? and (ii) How to optimally exploit these representations during the matching process? In this work, we train multiple instances of DeepPrint (a state-of-the-art DNN-based fingerprint encoder) on different transformations of the input image to generate an ensemble of fingerprint embeddings. We also propose a feature fusion technique that distills these multiple representations into a single embedding, which faithfully captures the diversity present in the ensemble without increasing the computational complexity. The proposed approach has been comprehensively evaluated on five databases containing rolled, plain, and latent fingerprints (NIST SD4, NIST SD14, NIST SD27, NIST SD302, and FVC2004 DB2A) and statistically significant improvements in accuracy have been consistently demonstrated across a range of verification as well as closed- and open-set identification settings. The proposed approach serves as a wrapper capable of improving the accuracy of any DNN-based recognition system.