Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/jspui/handle/123456789/10887
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dc.contributor.advisorGomes, David Menottipt_BR
dc.contributor.advisorMoreira, Gladston Juliano Pratespt_BR
dc.contributor.authorLuz, Eduardo José da Silva-
dc.date.accessioned2019-04-01T17:48:39Z-
dc.date.available2019-04-01T17:48:39Z-
dc.date.issued2019-
dc.identifier.citationLUZ, Eduardo José da Silva. Exploring deep learning representations for biometric multimodal systems. 2019. 134 f. Tese (Doutorado em Ciência da Computação) - Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, 2018.pt_BR
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/10887-
dc.descriptionPrograma de Pós-Graduação em Ciência da Computação. Departamento de Ciência da Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto.pt_BR
dc.description.abstractBiometrics is an important area of research today. A complete biometric system comprises sensors, feature extraction, pattern matching algorithms, and decision making. Biometric systems demand high accuracy and robustness, and researchers are using a combination of several biometric sources, two or more algorithms for pattern matching and di↵erent decision-making systems. These systems are called multimodal biometric systems and today represent state-of-the-art for biometrics. However, the process of extracting features in multimodal biometric systems poses a major challenge today. Deep learning has been used by researchers in the machine learning field to automatize the feature extraction process and several advances were achieved, such as the case of face recognition problem. However, deep learning based methods require a large amount of data and with the exception of facial recognition, there are no databases large enough for the other biometric modalities, hindering the application of deep learning in multimodal methods. In this thesis, we propose a set of contributions to favor the use of deep learning in multimodal biometric systems. First of all, we explore data augmentation and transfer learning techniques for training deep convolution networks, in restricted biometric databases in terms of labeled images. Second, we propose a simple protocol, aiming at reproducibility, for the creation and evaluation of multimodal (or synthetic) multimodal databases. This protocol allows the investigation of multiple biometric modalities combination, even for less common and novel modalities. Finally, we investigate the impact of merging multimodal biometric systems in which all modalities are represented by means of deep descriptors. In this work, we show that it is possible to bring the expressive gains already obtained with the face modality, to other four biometric modalities, by exploring deep learning techniques. We also show that the fusion of modalities is a promising path, even when they are represented by means of deep learning. We advance state-of-the-art for important databases in the literature, such as FRGC (periocular region), NICE / UBIRIS.V2 (periocular region and iris), MobBio (periocular region and face), CYBHi (o↵-the-person ECG), UofTDB (o↵-the-person ECG) and Physionet (EEG signal). Our best multimodal approach, on the chimeric database, resulted in the impressive decidability of 9.15±0.16 and a perfect recognition in (i.e., EER of 0.00%±0.00) for the intra-session multimodal scenario. For inter-session scenario, we reported decidability of 7.91±0.19 and an EER of 0.03%±0.03, which represents a gain of more than 22% for the best inter-session unimodal case.pt_BR
dc.language.isoen_USpt_BR
dc.rightsabertopt_BR
dc.subjectAprendizagempt_BR
dc.subjectBiometriapt_BR
dc.subjectTransferência de aprendizagempt_BR
dc.titleExploring deep learning representations for biometric multimodal systems.pt_BR
dc.typeTesept_BR
dc.rights.licenseAutorização concedida ao Repositório Institucional da UFOP pelo(a) autor(a) em 25/03/2019 com as seguintes condições: disponível sob Licença Creative Commons 4.0 que permite copiar, distribuir e transmitir o trabalho desde que sejam citados o autor e o licenciante. Não permite o uso para fins comerciais nem a adaptação.pt_BR
dc.contributor.refereeFerreira, Anderson Almeidapt_BR
dc.contributor.refereeMoreira, Gladston Juliano Pratespt_BR
dc.contributor.refereeGomes, David Menottipt_BR
dc.contributor.refereeCavalin, Paulopt_BR
dc.contributor.refereeCámara Chávez, Guillermopt_BR
dc.contributor.refereeSantos, Thiago Oliveira dospt_BR
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