# Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects

arXiv: https://arxiv.org/abs/2103.03038 · HTML: https://mlsanity.com/paper/2103.03038/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 44 references that are in the map.
- First posted on arXiv 4 Mar 2021.

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

- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [Matching Fingerphotos to Slap Fingerprint Images](https://mlsanity.com/paper/1804.08122/) (2018)
- [A Collaborative Approach using Ridge-Valley Minutiae for More Accurate Contactless Fingerprint Identification](https://mlsanity.com/paper/1909.06045/) (2019)

## Built on by (most cited first, 10 of 10)

- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation](https://mlsanity.com/paper/2204.03992/) (2022)
- [SynCoLFinGer: Synthetic Contactless Fingerprint Generator](https://mlsanity.com/paper/2110.09144/) (2021)
- [Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection](https://mlsanity.com/paper/2208.10688/) (2022)
- [Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network](https://mlsanity.com/paper/2212.05884/) (2022)
- [MCLFIQ: Mobile Contactless Fingerprint Image Quality](https://mlsanity.com/paper/2304.14123/) (2023)
- [Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction](https://mlsanity.com/paper/2311.15163/) (2023)
- [Fusion2Print: Deep Flash-Non-Flash Fusion for Contactless Fingerprint Matching](https://mlsanity.com/paper/2601.02318/) (2026)
- [TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging](https://mlsanity.com/paper/2501.05076/) (2025)
- [G-MSGINet: A Grouped Multi-Scale Graph-Involution Network for Contactless Fingerprint Recognition](https://mlsanity.com/paper/2505.08233/) (2025)

## Abstract (arXiv)

This work presents an automated touchless fingerprint recognition system for smartphones. We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system. Also, our implementation is made publicly available for research purposes. During a database acquisition, a total number of 1,360 touchless and touch-based samples of 29 subjects are captured in two different environmental situations. Experiments on the acquired database show a comparable performance of our touchless scheme and the touch-based baseline scheme under constrained environmental influences. A comparative usability study on both capturing device types indicates that the majority of subjects prefer the touchless capturing method. Based on our experimental results we analyze the impact of the current COVID-19 pandemic on fingerprint recognition systems. Finally, implementation aspects of touchless fingerprint recognition are summarized.