Exploring deep learning representations for biometric multimodal systems.
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2019
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Resumo
Biometrics 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.
Descrição
Programa de Pós-Graduação em Ciência da Computação. Departamento de Ciência da Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto.
Palavras-chave
Aprendizagem, Biometria, Transferência de aprendizagem
Citação
LUZ, 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.