{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:16:37Z","timestamp":1779383797747,"version":"3.53.1"},"reference-count":64,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T00:00:00Z","timestamp":1586217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011665","name":"Deanship of Scientific Research, King Saud University","doi-asserted-by":"publisher","award":["Graduate Students Research Support (GSR)"],"award-info":[{"award-number":["Graduate Students Research Support (GSR)"]}],"id":[{"id":"10.13039\/501100011665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.<\/jats:p>","DOI":"10.3390\/s20072085","type":"journal-article","created":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T05:59:47Z","timestamp":1586325587000},"page":"2085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection"],"prefix":"10.3390","volume":"20","author":[{"given":"Rami","family":"M. Jomaa","sequence":"first","affiliation":[{"name":"Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Mathkour","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9287-0596","authenticated-orcid":false,"given":"Yakoub","family":"Bazi","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2670-6007","authenticated-orcid":false,"given":"Md Saiful","family":"Islam","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mordini, E., and Tzovaras, D. 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