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About

I hold an integrated masters (bachelor+master) degree in Bioengineering, branch of Biomedical Engineering, focused on a wide array of subjects related to healthcare and engineering, such as sensors and electronics, signal and image processing, computer aided diagnosis, medical instruments, robotics and bionics, mobile programming, and software engineering.

I am currently a MSc Research Fellow at FEUP and Collaborator at INESC-TEC, and I focus mainly in signal and image analysis, combined with machine learning for pattern recognition applications. After studying and working on several projects focused on such technologies for the development and enhancement of medical diagnosis applications, I now work on biometrics, for human identification and authentication, mainly using electrocardiographic signals from off-the-person, highly noisy, unconstrained, and seamless acquisition settings.

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Publications

2018

Evolution, Current Challenges, and Future Possibilities in ECG Biometrics

Authors
Pinto, JR; Cardoso, JS; Lourenco, A;

Publication
IEEE ACCESS

Abstract
Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.

2017

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Authors
Pinto, JR; Cardoso, JS; Lourenco, A; Carreiras, C;

Publication
SENSORS

Abstract
Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method's performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.