Fingerprint liveness detection using local quality features

TL;DR

This paper introduces a static, software-based fingerprint liveness detection method using local quality features from a single image, achieving high accuracy and faster processing compared to dynamic approaches.

Contribution

The paper presents a novel local quality feature-based approach for fingerprint liveness detection that is static, sensor-independent, and more efficient than existing dynamic methods.

Findings

Achieved 5.3% error rate on LivDet 2009 dataset.

Achieved 4.22% error rate on LivDet 2015 dataset.

Outperformed current state-of-the-art methods in accuracy.

Abstract

Fingerprint-based recognition has been widely deployed in various applications. However, current recognition systems are vulnerable to spoofing attacks which make use of an artificial replica of a fingerprint to deceive the sensors. In such scenarios, fingerprint liveness detection ensures the actual presence of a real legitimate fingerprint in contrast to a fake self-manufactured synthetic sample. In this paper, we propose a static software-based approach using quality features to detect the liveness in a fingerprint. We have extracted features from a single fingerprint image to overcome the issues faced in dynamic software-based approaches which require longer computational time and user cooperation. The proposed system extracts 8 sensor independent quality features on a local level containing minute details of the ridge-valley structure of real and fake fingerprints. These local quality features constitutes a 13-dimensional feature vector. The system is tested on a publically available dataset of LivDet 2009 competition. The experimental results exhibit supremacy of the proposed method over current state-of-the-art approaches providing least average classification error of 5.3% for LivDet 2009. Additionally, effectiveness of the best performing features over LivDet 2009 is evaluated on the latest LivDet 2015 dataset which contain fingerprints fabricated using unknown spoof materials. An average classification error rate of 4.22% is achieved in comparison with 4.49% obtained by the LivDet 2015 winner. Further, the proposed system utilizes a single fingerprint image, which results in faster implications and makes it more user-friendly.