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About

I am graduated in Biomedical Engineering at Politécnico do Porto. Besides, I obtained the MSc degree in Biomedical Engenerring at Faculdade de Engenharia da Universidade do Porto (FEUP).

Currently, I am a Researcher at INESC TEC and a PhD student enrolled in the Doctoral Program in Electrical and Computer Engineering at FEUP.

My main research interests include Computer Vision, Machine Learning and Artificial Intelligence.

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Publications

2018

Physiological Inspired Deep Neural Networks for Emotion Recognition

Authors
Ferreira, PM; Marques, F; Cardoso, JS; Rebelo, A;

Publication
IEEE ACCESS

Abstract
Facial expression recognition (FER) is currently one of the most active research topics due to its wide range of applications in the human-computer interaction field. An important part of the recent success of automatic FER was achieved thanks to the emergence of deep learning approaches. However, training deep networks for FER is still a very challenging task, since most of the available FER data sets are relatively small. Although transfer learning can partially alleviate the issue, the performance of deep models is still below of its full potential as deep features may contain redundant information from the pre-trained domain. Instead, we propose a novel end-to-end neural network architecture along with a well-designed loss function based on the strong prior knowledge that facial expressions are the result of the motions of some facial muscles and components. The loss function is defined to regularize the entire learning process so that the proposed neural network is able to explicitly learn expression-specific features. Experimental results demonstrate the effectiveness of the proposed model in both lab-controlled and wild environments. In particular, the proposed neural network provides quite promising results, outperforming in most cases the current state-of-the-art methods.

2018

Robust Clustering-based Segmentation Methods for Fingerprint Recognition

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

Publication
2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018

Abstract
Fingerprint recognition has been widely studied for more than 45 years and yet it remains an intriguing pattern recognition problem. This paper focuses on the foreground mask estimation which is crucial for the accuracy of a fingerprint recognition system. The method consists of a robust cluster-based fingerprint segmentation framework incorporating an additional step to deal with pixels that were rejected as foreground in a decision considered not reliable enough. These rejected pixels are then further analysed for a more accurate classification. The procedure falls in the paradigm of classification with reject option- a viable option in several real world applications of machine learning and pattern recognition, where the cost of misclassifying observations is high. The present work expands a previous method based on the fuzzy C-means clustering with two variations regarding: i) the filters used; and ii) the clustering method for pixel classification as foreground/background. Experimental results demonstrate improved results on FVC datasets comparing with state-of-the-art methods even including methodologies based on deep learning architectures. © 2018 Gesellschaft fuer Informatik.

2017

Multimodal Learning for Sign Language Recognition

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

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

Abstract

2015

A Fuzzy C-Means Algorithm for Fingerprint Segmentation

Authors
Ferreira, PM; Sequeira, AF; Rebelo, A;

Publication
PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015)

Abstract
Fingerprint segmentation is a crucial step of an automatic fingerprint identification system, since an accurate segmentation promote both the elimination of spurious minutiae close to the foreground boundaries and the reduction of the computation time of the following steps. In this paper, a new, and more robust fingerprint segmentation algorithm is proposed. The main novelty is the introduction of a more robust binarization process in the framework, mainly based on the fuzzy C-means clustering algorithm. Experimental results demonstrate significant benchmark progress on three existing FVC datasets.

2015

PH2: A Public Database for the Analysis of Dermoscopic Images

Authors
Mendonça, T; Ferreira, P; Marçal, A; Barata, C; Marques, J; Rocha, J; Rozeira, J;

Publication
Dermoscopy Image Analysis - Digital Imaging and Computer Vision

Abstract