Fingerprint Liveness Detection Based on Quality Measures

TL;DR

This paper introduces a fingerprint liveness detection method based on quality measures, achieving high accuracy with a single image and demonstrating robustness across multiple sensors.

Contribution

The paper presents a novel quality-based feature set for fingerprint liveness detection that works effectively with just one image and across different optical sensors.

Findings

93% accuracy in classifying real and fake fingerprints

Robust performance across multiple sensor types

Requires only one image per fingerprint

Abstract

A new fingerprint parameterization for liveness detection based on quality measures is presented. The novel feature set is used in a complete liveness detection system and tested on the development set of the LivDET competition, comprising over 4,500 real and fake images acquired with three different optical sensors. The proposed solution proves to be robust to the multi-sensor scenario, and presents an overall rate of 93% of 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.