# A high performance fingerprint liveness detection method based on quality related features

arXiv: https://arxiv.org/abs/2111.01898 · HTML: https://mlsanity.com/paper/2111.01898/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 61 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Direct attacks using fake images in iris verification](https://mlsanity.com/paper/2111.00178/) (2021)

## Built on by (most cited first, 5 of 5)

- [An Iterative Algorithm for Rescaled Hyperbolic Functions Regression](https://mlsanity.com/paper/2305.00660/) (2023)
- [Convergence of Two-Layer Regression with Nonlinear Units](https://mlsanity.com/paper/2308.08358/) (2023)
- [Face Presentation Attack Detection](https://mlsanity.com/paper/2212.03680/) (2022)
- [Introduction to Presentation Attack Detection in Fingerprint Biometrics](https://mlsanity.com/paper/2304.06723/) (2023)
- [Adaptive thresholding pattern for fingerprint forgery detection](https://mlsanity.com/paper/2511.15322/) (2025)

## Abstract (arXiv)

A new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed. The system is tested on a highly challenging database comprising over 10,500 real and fake images acquired with five sensors of different technologies and covering a wide range of direct attack scenarios in terms of materials and procedures followed to generate the gummy fingers. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake. This last characteristic provides the method with very valuable features as it makes it less intrusive, more user friendly, faster and reduces its implementation costs.