Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About
Download Photo HD

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 Research Assistant at CTM - INESC TEC and a PhD student at FEUP, conducting research in signal and image analysis, combined with machine learning for pattern recognition applications. My work focuses mainly on biometrics, for human identification and authentication, using electrocardiographic signals from off-the-person, highly noisy, unconstrained, and seamless acquisition settings.

Interest
Topics
Details

Details

002
Publications

2020

Secure Triplet Loss for End-to-End Deep Biometrics

Authors
Pinto, JR; Cardoso, JS; Correia, MV;

Publication
2020 8th International Workshop on Biometrics and Forensics (IWBF)

Abstract

2019

Don’t You Forget About Me: A Study on Long-Term Performance in ECG Biometrics

Authors
Lopes, G; Pinto, JR; Cardoso, JS;

Publication
Pattern Recognition and Image Analysis - Lecture Notes in Computer Science

Abstract

2019

Deep Neural Networks for Biometric Identification Based on Non-Intrusive ECG Acquisitions

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

Publication
The Biometric Computing

Abstract

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.

2018

A Uniform Performance Index for Ordinal Classification with Imbalanced Classes

Authors
Silva, W; Pinto, JR; Cardoso, JS;

Publication
2018 International Joint Conference on Neural Networks (IJCNN)

Abstract