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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

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
Afonso Pereira, J; 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.).

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.

Supervised
thesis

2020

Adversarial Domain Adaptation for Sensor Networks

Author
Francisco Tuna de Andrade

Institution
UP-FEUP