# MCLFIQ: Mobile Contactless Fingerprint Image Quality

arXiv: https://arxiv.org/abs/2304.14123 · HTML: https://mlsanity.com/paper/2304.14123/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [SynCoLFinGer: Synthetic Contactless Fingerprint Generator](https://mlsanity.com/paper/2110.09144/) (2021)
- [Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects](https://mlsanity.com/paper/2103.03038/) (2021)
- [Matching Fingerphotos to Slap Fingerprint Images](https://mlsanity.com/paper/1804.08122/) (2018)

## Built on by (most cited first, 1 of 1)

- [Cross-Modal Registration Between 3D and 2D Fingerprints via Pose-Aware Unwrapping and Point-Cloud Fusion](https://mlsanity.com/paper/2605.15796/) (2026)

## Abstract (arXiv)

We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we re-trained the NIST Fingerprint Image Quality (NFIQ) 2 method, which was originally designed for contact-based fingerprints, with a synthetic contactless fingerprint database. We evaluate the predictive performance of the resulting MCLFIQ model in terms of Error-vs.-Discard Characteristic (EDC) curves on three real-world contactless fingerprint databases using three recognition algorithms. In experiments, the MCLFIQ method is compared against the original NFIQ 2.2 method, a sharpness-based quality assessment algorithm developed for contactless fingerprint images revand the general purpose image quality assessment method BRISQUE. Furthermore, benchmarks on four contact-based fingerprint datasets are also conducted. Obtained results show that the fine-tuning of NFIQ 2 on synthetic contactless fingerprints is a viable alternative to training on real databases. Moreover, the evaluation shows that our MCLFIQ method works more accurate and robust compared to all baseline methods on contactless fingerprints. We suggest considering the proposed MCLFIQ method as a revstarting point for the development of a new standard algorithm for contactless fingerprint quality assessment.