Learning deep off-the-person heart biometrics representations.

dc.contributor.authorLuz, Eduardo José da Silva
dc.contributor.authorMoreira, Gladston Juliano Prates
dc.contributor.authorOliveira, Luiz Eduardo Soares de
dc.contributor.authorSchwartz, William Robson
dc.contributor.authorGomes, David Menotti
dc.date.accessioned2018-10-18T16:29:10Z
dc.date.available2018-10-18T16:29:10Z
dc.date.issued2018
dc.description.abstractSince the beginning of the new millennium, the electrocardiogram (ECG) has been studied as a biometric trait for security systems and other applications. Recently, with devices such as smartphones and tablets, the acquisition of ECG signal in the off-the-person category has made this biometric signal suitable for real scenarios. In this paper, we introduce the usage of deep learning techniques, specifically convolutional networks, for extracting useful representation for heart biometrics recognition. Particularly, we investigate the learning of feature representations for heart biometrics through two sources: on the raw heartbeat signal and on the heartbeat spectrogram. We also introduce heartbeat data augmentation techniques, which are very important to generalization in the context of deep learning approaches. Using the same experimental setup for six methods in the literature, we show that our proposal achieves state-of-the-art results in the two off-the-person publicly available databases.pt_BR
dc.identifier.citationLUZ, E. J. da S. et al. Learning deep off-the-person heart biometrics representations. IEEE Transactions on Information Forensics and Security, v. 13, n. 5, p. 1258-1270, mai. 2018. Disponível em: <https://ieeexplore.ieee.org/document/8219706/>. Acesso em: 16 jun. 2018.pt_BR
dc.identifier.issn15566013
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/10397
dc.identifier.uri2https://ieeexplore.ieee.org/document/8219706/authorspt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectElectrocardiogrampt_BR
dc.subjectOff-the-person categorypt_BR
dc.subjectBiometric systemspt_BR
dc.subjectDeep learningpt_BR
dc.titleLearning deep off-the-person heart biometrics representations.pt_BR
dc.typeArtigo publicado em periodicopt_BR
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
ARTIGO_LearningDeepPerson.pdf
Size:
3.38 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
924 B
Format:
Item-specific license agreed upon to submission
Description: