A Contactless Fingerprint Recognition System

TL;DR

This paper introduces a real-time contactless fingerprint recognition system using CNNs and minutiae features, addressing hygiene and sensor degradation issues of contact-based systems, and demonstrates its effectiveness on a custom dataset.

Contribution

It presents a novel contactless fingerprint recognition approach utilizing CNNs and feature fusion, implemented on Nvidia Jetson Nano for real-time performance.

Findings

Achieved an equal-error-rate of 2.19% on IITI-CFD dataset.

Demonstrated real-time recognition with minimal latency.

Validated effectiveness of contactless system over traditional methods.

Abstract

Fingerprints are one of the most widely explored biometric traits. Specifically, contact-based fingerprint recognition systems reign supreme due to their robustness, portability and the extensive research work done in the field. However, these systems suffer from issues such as hygiene, sensor degradation due to constant physical contact, and latent fingerprint threats. In this paper, we propose an approach for developing a contactless fingerprint recognition system that captures finger photo from a distance using an image sensor in a suitable environment. The captured finger photos are then processed further to obtain global and local (minutiae-based) features. Specifically, a Siamese convolutional neural network (CNN) is designed to extract global features from a given finger photo. The proposed system computes matching scores from CNN-based features and minutiae-based features. Finally, the two scores are fused to obtain the final matching score between the probe and reference fingerprint templates. Most importantly, the proposed system is developed using the Nvidia Jetson Nano development kit, which allows us to perform contactless fingerprint recognition in real-time with minimum latency and acceptable matching accuracy. The performance of the proposed system is evaluated on an in-house IITI contactless fingerprint dataset (IITI-CFD) containing 105train and 100 test subjects. The proposed system achieves an equal-error-rate of 2.19% on IITI-CFD.