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

TL;DR

This paper introduces a robust, software-based fingerprint liveness detection method using quality-related features, achieving high accuracy with just one image and demonstrating effectiveness across diverse sensors and attack scenarios.

Contribution

The paper presents a novel fingerprint liveness detection approach that requires only a single image and is effective across multiple sensors and attack types.

Findings

90% correct classification rate on challenging dataset

Effective across five different sensor technologies

Requires only one fingerprint image for detection

Abstract

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.