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Fingerprint presentation attack detection utilizing spatio-temporal features

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2021-03-02
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MDPI
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Abstract
This paper presents a novel mechanism for fingerprint dynamic presentation attack detec-tion. We utilize five spatio-temporal feature extractors to efficiently eliminate and mitigate different presentation attack species. The feature extractors are selected such that the fingerprint ridge/valley pattern is consolidated with the temporal variations within the pattern in fingerprint videos. An SVM classification scheme, with a second degree polynomial kernel, is used in our presentation attack detection subsystem to classify bona fide and attack presentations. The experiment protocol and evaluation are conducted following the ISO/IEC 30107-3:2017 standard. Our proposed approach demonstrates efficient capability of detecting presentation attacks with significantly low BPCER where BPCER is 1.11% for an optical sensor and 3.89% for a thermal sensor at 5% APCER for both.
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This article belongs to the Special Issue Biometric Sensing.
Keywords
Anti-spoofing, Fingerprint, Presentation attack, Presentation attack detection
Bibliographic citation
Husseis, A., Liu-Jimenez, J. & Sanchez-Reillo, R. (2021). Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features. Sensors, 21(6), 2059.