RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset

TL;DR

RidgeBase is a large, diverse contactless fingerprint dataset designed to advance research in contactless fingerprint recognition across different sensors and scenarios, including multi-finger and cross-sensor matching.

Contribution

It introduces a comprehensive dataset with over 15,000 fingerprint pairs, a set-based matching protocol, and baseline evaluations for contactless fingerprint recognition.

Findings

Baseline results demonstrate the dataset's utility for developing robust contactless fingerprint matchers.

The set-based protocol effectively handles intra-sample variance in contactless fingerprints.

The dataset supports various matching scenarios, promoting versatile research in contactless biometrics.

Abstract

Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presentation attacks. However, development of practical and robust contactless fingerprint matching techniques is constrained by the limited availability of large scale real-world datasets. To motivate further advances in contactless fingerprint matching across sensors, we introduce the RidgeBase benchmark dataset. RidgeBase consists of more than 15,000 contactless and contact-based fingerprint image pairs acquired from 88 individuals under different background and lighting conditions using two smartphone cameras and one flatbed contact sensor. Unlike existing datasets, RidgeBase is designed to promote research under different matching scenarios that include Single Finger Matching and Multi-Finger Matching for both contactless- to-contactless (CL2CL) and contact-to-contactless (C2CL) verification and identification. Furthermore, due to the high intra-sample variance in contactless fingerprints belonging to the same finger, we propose a set-based matching protocol inspired by the advances in facial recognition datasets. This protocol is specifically designed for pragmatic contactless fingerprint matching that can account for variances in focus, polarity and finger-angles. We report qualitative and quantitative baseline results for different protocols using a COTS fingerprint matcher (Verifinger) and a Deep CNN based approach on the RidgeBase dataset. The dataset can be downloaded here: https://www.buffalo.edu/cubs/research/datasets/ridgebase-benchmark-dataset.html