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

arXiv: https://arxiv.org/abs/2307.05563 · HTML: https://mlsanity.com/paper/2307.05563/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations](https://mlsanity.com/paper/1905.00292/) (2019)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [Matching Fingerphotos to Slap Fingerprint Images](https://mlsanity.com/paper/1804.08122/) (2018)

## Built on by (most cited first, 9 of 9)

- [CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion](https://mlsanity.com/paper/2307.10237/) (2023)
- [Deep Metric Learning for Computer Vision: A Brief Overview](https://mlsanity.com/paper/2312.10046/) (2023)
- [Liveness Detection Competition -- Noncontact-based Fingerprint Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)](https://mlsanity.com/paper/2310.00659/) (2023)
- [Ridgeformer: Mutli-Stage Contrastive Training For Fine-grained Cross-Domain Fingerprint Recognition](https://mlsanity.com/paper/2506.01806/) (2025)
- [Fusion2Print: Deep Flash-Non-Flash Fusion for Contactless Fingerprint Matching](https://mlsanity.com/paper/2601.02318/) (2026)
- [X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication](https://mlsanity.com/paper/2606.08437/) (2026)
- [UFQA: Utility guided Fingerphoto Quality Assessment](https://mlsanity.com/paper/2407.11141/) (2024)
- [FDWST: Fingerphoto Deblurring using Wavelet Style Transfer](https://mlsanity.com/paper/2407.15964/) (2024)
- [Illumination-Aware Contactless Fingerprint Spoof Detection via Paired Flash-Non-Flash Imaging](https://mlsanity.com/paper/2603.17679/) (2026)

## Abstract (arXiv)

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