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About

I was born in Porto, Portugal, in 1990. I graduated in Electrical and Computers Engineering (Master's Degree) at FEUP (Faculty of Engineering, University of Porto) in 2014.

My first collaboration with INESC TEC happened in 2013, when I joined the VCMI group (Visual Computing and Machine Intelligence) to work in the development of a software tool for breast surgery planning.

From 2014 to 2016, I worked at Synopsys, Inc. as an ASIC Digital Design Engineer. The main purpose of my work was the development of System Verilog verification environments for specific interface intelectual property standards.

I came back to INESC TEC and to the VCMI group in January 2017. Right now, my research is mainly focused on Machine Learning foundamental topics, with some application to Computer Vision.

Interest
Topics
Details

Details

002
Publications

2022

Tackling unsupervised multi-source domain adaptation with optimism and consistency

Authors
Pernes, D; Cardoso, JS;

Publication
Expert Systems with Applications

Abstract
It has been known for a while that the problem of multi-source domain adaptation can be regarded as a single source domain adaptation task where the source domain corresponds to a mixture of the original source domains. Nonetheless, how to adjust the mixture distribution weights remains an open question. Moreover, most existing work on this topic focuses only on minimizing the error on the source domains and achieving domain-invariant representations, which is insufficient to ensure low error on the target domain. In this work, we present a novel framework that addresses both problems and beats the current state of the art by using a mildly optimistic objective function and consistency regularization on the target samples. © 2022 Elsevier Ltd

2021

DeSIRe: Deep Signer-Invariant Representations for Sign Language Recognition

Authors
Ferreira, PM; Pernes, D; Rebelo, A; Cardoso, JS;

Publication
IEEE Transactions on Systems, Man, and Cybernetics: Systems

Abstract

2021

Hidden Markov models on a self-organizing map for anomaly detection in 802.11 wireless networks

Authors
Allahdadi, A; Pernes, D; Cardoso, JS; Morla, R;

Publication
Neural Computing and Applications

Abstract

2020

Learning signer-invariant representations with adversarial training

Authors
Ferreira, PM; Pernes, D; Rebelo, A; Cardoso, JS;

Publication
Twelfth International Conference on Machine Vision (ICMV 2019)

Abstract

2020

A robust fingerprint presentation attack detection method against unseen attacks through adversarial learning

Authors
Pereira, JA; Sequeira, AF; Pernes, D; Cardoso, JS;

Publication
BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group

Abstract
Fingerprint presentation attack detection (PAD) methods present a stunning performance in current literature. However, the fingerprint PAD generalisation problem is still an open challenge requiring the development of methods able to cope with sophisticated and unseen attacks as our eventual intruders become more capable. This work addresses this problem by applying a regularisation technique based on an adversarial training and representation learning specifically designed to to improve the PAD generalisation capacity of the model to an unseen attack. In the adopted approach, the model jointly learns the representation and the classifier from the data, while explicitly imposing invariance in the high-level representations regarding the type of attacks for a robust PAD. The application of the adversarial training methodology is evaluated in two different scenarios: i) a handcrafted feature extraction method combined with a Multilayer Perceptron (MLP); and ii) an end-to-end solution using a Convolutional Neural Network (CNN). The experimental results demonstrated that the adopted regularisation strategies equipped the neural networks with increased PAD robustness. The adversarial approach particularly improved the CNN models' capacity for attacks detection in the unseen-attack scenario, showing remarkable improved APCER error rates when compared to state-of-the-art methods in similar conditions. © 2020 German Computer Association (Gesellschaft für Informatik e.V.).

Supervised
thesis

2020

Adversarial Domain Adaptation for Sensor Networks

Author
Francisco Tuna de Andrade

Institution
UP-FEUP