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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.

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002
Publications

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

2019

Directional support vector machines

Authors
Pernes, D; Fernande, K; Cardoso, JS;

Publication
Applied Sciences (Switzerland)

Abstract
Several phenomena are represented by directional-angular or periodic-data; from time references on the calendar to geographical coordinates. These values are usually represented as real values restricted to a given range (e.g., [0, 2p)), hiding the real nature of this information. In order to handle these variables properly in supervised classification tasks, alternatives to the naive Bayes classifier and logistic regression were proposed in the past. In this work, we propose directional-aware support vector machines. We address several realizations of the proposed models, studying their kernelized counterparts and their expressiveness. Finally, we validate the performance of the proposed Support Vector Machines (SVMs) against the directional naive Bayes and directional logistic regression with real data, obtaining competitive results. © 2019 by the authors.

2019

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

2019

SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

Authors
Perues, D; Cardoso, JS;

Publication
Proceedings of the International Joint Conference on Neural Networks

Abstract
We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are jointly trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method. © 2019 IEEE.

2019

Adversarial learning for a robust iris presentation attack detection method against unseen attack presentations

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

Publication
2019 International Conference of the Biometrics Special Interest Group, BIOSIG 2019 - Proceedings

Abstract
Despite the high performance of current presentation attack detection (PAD) methods, the robustness to unseen attacks is still an under addressed challenge. This work approaches the problem by enforcing the learning of the bona fide presentations while making the model less dependent on the presentation attack instrument species (PAIS). The proposed model comprises an encoder, mapping from input features to latent representations, and two classifiers operating on these underlying representations: (i) the task-classifier, for predicting the class labels (as bona fide or attack); and (ii) the species-classifier, for predicting the PAIS. In the learning stage, the encoder is trained to help the task-classifier while trying to fool the species-classifier. Plus, an additional training objective enforcing the similarity of the latent distributions of different species is added leading to a 'PAI-species'-independent model. The experimental results demonstrated that the proposed regularisation strategies equipped the neural network with increased PAD robustness. The adversarial model obtained better loss and accuracy as well as improved error rates in the detection of attack and bona fide presentations. © 2019 Gesellschaft fuer Informatik.