{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:49:57Z","timestamp":1767422997668,"version":"build-2065373602"},"reference-count":72,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal\u2019s biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the (p,q)-planes specific to the 0\u20131 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed\u2019s biostructure, unique to each individual. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. Biometric system results for input data, raw and preprocessed, are studied and compared with eight primary biometric methods related to PPG, achieving the best equal error rate (ERR) and processing times with a single attempt, among all of them.<\/jats:p>","DOI":"10.3390\/s21165661","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T10:24:17Z","timestamp":1629714257000},"page":"5661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Transcending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2844-1858","authenticated-orcid":false,"given":"Javier","family":"de Pedro-Carracedo","sequence":"first","affiliation":[{"name":"Departamento de Tecnolog\u00eda Fot\u00f3nica y Bioingenier\u00eda, ETSI Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid (UPM), E-28040 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6424-4782","authenticated-orcid":false,"given":"David","family":"Fuentes-Jimenez","sequence":"additional","affiliation":[{"name":"Departamento de Electr\u00f3nica, Universidad de Alcal\u00e1 (UAH), Escuela Polit\u00e9cnica Superior, Alcal\u00e1 de Henares (Madrid), E-28871 Alcal\u00e1 de Henares, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7596-6756","authenticated-orcid":false,"given":"Ana Mar\u00eda","family":"Ugena","sequence":"additional","affiliation":[{"name":"Departamento de Matem\u00e1tica Aplicada a las Tecnolog\u00edas de la Informaci\u00f3n, ETSI Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid (UPM), E-28040 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0757-2445","authenticated-orcid":false,"given":"Ana Pilar","family":"Gonzalez-Marcos","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00eda Fot\u00f3nica y Bioingenier\u00eda, ETSI Telecomunicaci\u00f3n, Universidad Polit\u00e9cnica de Madrid (UPM), E-28040 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/MCE.2020.3002521","article-title":"Post COVID-19 Thoughts: Controversies and Merits of the Technology Progress","volume":"9","author":"Velikic","year":"2020","journal-title":"IEEE Consum. 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