{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T21:01:46Z","timestamp":1722718906537},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T00:00:00Z","timestamp":1615766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["675087"],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"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.<\/jats:p>","DOI":"10.3390\/s21062059","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T02:16:54Z","timestamp":1615861014000},"page":"2059","source":"Crossref","is-referenced-by-count":8,"title":["Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6327-5659","authenticated-orcid":false,"given":"Anas","family":"Husseis","sequence":"first","affiliation":[{"name":"University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, 28911 Madrid, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1938-0309","authenticated-orcid":false,"given":"Judith","family":"Liu-Jimenez","sequence":"additional","affiliation":[{"name":"University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, 28911 Madrid, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4239-985X","authenticated-orcid":false,"given":"Raul","family":"Sanchez-Reillo","sequence":"additional","affiliation":[{"name":"University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, 28911 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"key":"ref_1","unstructured":"(2021, February 03). 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