Check out the Science Bits podcast on Biometrics with Ana Filipa Sequeira and me!
Sara and I have been selected as "Incredible" at INESC TEC in July. Thank you!
New paper on interpretability for ECG biometrics accepted at BIOSIG 2020!
Presenting my self-learning paper at IJCNN 2020. Amazing to be amidst so much knowledge!
My colleague's paper on breast cancer HER2 classification has been accepted! Congrats Sara!
Best paper award at IWBF 2020 for my work on secure triplet loss. Thanks everyone!
about me

Hi, my name is João. I am a research assistant at INESC TEC, Porto, Portugal, and third-year PhD Student in Electrical and Computers Engineering at Faculdade de Engenharia da Universidade do Porto (FEUP). In 2017, I have completed my integrated masters degree in Bioengineering (field of Biomedical Engineering) with a thesis on the use of the electrocardiogram (ECG) for biometric identification of vehicle drivers.

Since then, my research has focused on contributing to make the ECG a viable and stronger biometric trait in realistic conditions. Hence, my PhD studies are focused on using ECG and face video, both acquired almost unnoticeably from vehicle drivers, to recognise them and continuously monitor their drowsiness and emotions. Additionally, I frequently work on other topics related to biometrics, computer vision, and machine learning in general. To do this, I rely on my knowledge and experience in machine learning, signal and image processing, and electronics.

Welcome to my website, where I present a little bit more about myself and my work. Feel free to contact me with any question or proposal, I am always looking for new opportunities to develop my skills and expand my knowledge.

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Biometric recognition is the art of recognising individuals based on intrinsic characteristics, like fingerprints, face video, or iris images. These characteristics are always carried by the person, and are harder to forget, misplace, steal, or copy than ID cards or passwords.

My research is focused on developing more effective, robust, and secure biometric systems. I have focused mainly on the electrocardiogram as a biometric trait, given its hidden nature that makes it more difficult to steal or counterfeit. However, I have also worked on other traits, especially face image and video.

While biometric recognition is the main focus of my research, I have also delved into the topics of template security, presentation attack detection and, more recently, the use of interpretability tools to explain decisions given by deep biometric models.

Pattern Recognition

When looking at a picture, humans immediately recognise there are people in it, maybe some trees, a house, and the sky in the background. For a computer, however, a picture is no more than a matrix of organised numeric values. The same applies to signals, video, or any other data.

In Pattern Recognition, a subarea of Artificial Intelligence, researchers focus on making computers able to detect, discriminate, and identify patterns in data. For that, we use signal processing, image analysis, and/or machine learning methods for computers to learn to understand data.

My research focuses mainly in pattern recognition for biometrics. However, I have also worked on image analysis for automatic medical diagnosis, and on more fundamental machine learning topics including weakly-supervised learning and ordinal classification.


The electrocardiogram (ECG) is the signal that measures the flow of electrical currents across the heart. It is not only a key tool for clinical diagnosis of heart conditions, but a carrier of valuable personal information on identity, health, and emotional states.

Through my research, I create algorithms that use the ECG for automatic recognition of identity, emotions, and fatigue in human-computer interfaces. Specifically, I focus on challenging off-the-person signals acquired unobtrusively, through electrodes seamlessly integrated in common objects such as wearables, smartphones, or steering wheels.

One of my main goals is to make the ECG biometric algorithms as effective and robust as face or fingerprint biometrics, while reataining the higher security it offers as a hidden biometric trait.

Wellbeing Monitoring

The variability of a person's traits is often a nuisance for biometric recognition. However, it often reveals valuable information that could be used to understand other aspects and characteristics of the subjects, including their health and emotional states.

Face image and video has long been used to understand facial expressions and link them to emotional states, despite claims of unreliability. Physiological traits like the ECG include more intrinsic information on health and emotions, that is much harder to fake than facial expressions.

My research in wellbeing monitoring is mainly focused on combining ECG and face video information for more robust emotion, stress, and fatigue recognition. This work is especially focused on human-machine interface contexts, e.g., for real-time monitoring of vehicle drivers.

selected publications
Explaining ECG Biometrics: Is It All In The QRS? 19th International Conference of the Biometrics Special Interest Group (BIOSIG 2020) J. R. Pinto and J. S. Cardoso 2020
Self-Learning with Stochastic Triplet Loss International Joint Conference on Neural Networks (IJCNN 2020) J. R. Pinto and J. S. Cardoso 2020
Secure Triplet Loss for End-to-End Deep Biometrics 8th International Workshop on Biometrics and Forensics (IWBF 2020) J. R. Pinto, J. S. Cardoso, and M. V. Correia 2020
An End-to-End Convolutional Neural Network for ECG-Based Biometric Authentication 10th IEEE International Conference in Biometrics: Theory, Applications and Systems (BTAS '19) J. R. Pinto and J. S. Cardoso 2019
Evolution, Current Challenges, and Future Possibilities in ECG Biometrics IEEE Access 6 J. R. Pinto, J. S. Cardoso, and A. Lourenço 2018
22 publications
120 citations
4 h-index
2 i10
see full list
supervised msc students current students (2)
Co-supervisor of Sofia C. Beco (2020/21)
"Make My Heartbeat: Generation and Interlead Conversion of ECG Signals", Master in Bioengineering, Universidade do Porto
Co-supervisor of Inês A. T. A. Magalhães (2020/21)
"Feel My Heart: Emotion Recognition Using the Electrocardiogram", Master in Bioengineering, Universidade do Porto
past students (4)
Co-supervisor of Arthur J. Matta (2019/20)
"Deep Learning For Face Recognition: A Real-Time Face Biometric Recognition System", Master in Informatics and Computing Engineering, Universidade do Porto
Co-supervisor of Carolina M. B. R. Afonso (2019/20)
"Changing Perspectives: Interlead Conversion in Electrocardiographic Signals", Master in Network and Information Systems Engineering, Universidade do Porto
Co-supervisor of Leonardo G. Capozzi (2019/20)
"Face Recognition For Forensic Applications: Methods for Matching Facial Sketches to Mugshot Pictures", Master in Informatics and Computing Engineering, Universidade do Porto
Co-supervisor of Gabriel C. Lopes (2018/19)
"Don't You Forget About Me: Enhancing Long Term Performance in Electrocardiogram Biometrics", Master in Bioengineering, Universidade do Porto
supervised internships past (11)
Co-supervisor of João Fonseca (2020)
NEB Outclass Internship: Face Photograph Analytics
Co-supervisor of Catarina Lopes (2019-2020)
Face photograph analytics for automatic assessment of ICAO guideline compliance, INESC TEC
Co-supervisor of Pedro Silva, Paulo Costa, Manuel Curral, Gil Teixeira, Pedro Neto, Martim Silva, João Mendes, João Moura, and Carolina Afonso (July 2019)
Various topics related to face biometrics, forensics, and analytics, CTM Summer Internships 2019, INESC TEC
research projects current (2)
EasyRide Project (Collaborator since February 2020)
In-vehicle activity recognition and wellbeing measurement.
AUTOMOTIVE Project (Collaborator since July 2019)
Driver drowsiness monitoring using unconstrained video and physiological signals.
past (1)
CHIC Project (2018/19)
Quality assessment of print text documents for optical character and image recognition.
my cv full pdf
Research Assistant
PhD Student in Biometrics
INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias 4200-465 Porto, Portugal +351 22 209 4000
INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias 4200-465 Porto, Portugal +351 22 209 4000