Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features.
This paper presents a novel mechanism for fingerprint dynamic presentation attack detection. 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.
['Husseis|Anas|A|0000-0001-6327-5659', 'Liu-Jimenez|Judith|J|0000-0003-1938-0309', 'Sanchez-Reillo|Raul|R|0000-0003-4239-985X']
fingerprint presentation attack presentation attack detection anti-spoofing