Browsing by Author "Oliveira, Luiz Eduardo Soares de"

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    Learning deep off-the-person heart biometrics representations.
    (2018) Luz, Eduardo José da Silva; Moreira, Gladston Juliano Prates; Oliveira, Luiz Eduardo Soares de; Schwartz, William Robson; Gomes, David Menotti
    Since 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.
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    Ocular recognition databases and competitions : a survey.
    (2022) Zanlorensi Junior, Luiz Antonio; Laroca, Rayson; Luz, Eduardo José da Silva; Britto Junior, Alceu de Souza; Oliveira, Luiz Eduardo Soares de; Menotti, David
    The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufcient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other information such as the individual’s gender, the infuence of drug use, the use of contact lenses, spoofng, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specifc problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.