Fingerprint liveness detection using local quality features
Ram Prakash Sharma, Somnath Dey

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…
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