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About

Jaime S. Cardoso holds a Licenciatura (5-year degree) in Electrical and Computer Engineering in 1999, an MSc in Mathematical Engineering in 2005 and a Ph.D. in Computer Vision in 2006, all from the University of Porto.

Cardoso is an Associate Professor with Habilitation at the Faculty of Engineering of the University of Porto (FEUP), where he has been teaching Machine Learning and Computer Vision in Doctoral Programs and multiple courses for the graduate studies. Cardoso is currently a Senior Researcher of the ‘Information Processing and Pattern Recognition’ Area in the Telecommunications and Multimedia Unit of INESC TEC. He is also Senior Member of IEEE and co-founder of ClusterMedia Labs, an IT company developing automatic solutions for semantic audio-visual analysis.

His research can be summed up in three major topics: computer vision, machine learning and decision support systems.  Cardoso has co-authored 150+ papers, 50+ of which in international journals. Cardoso has been the recipient of numerous awards, including the Honorable Mention in the Exame Informática Award 2011, in software category, for project “Semantic PACS” and the First Place in the ICDAR 2013 Music Scores Competition: Staff Removal (task: staff removal with local noise), August 2013. The research results have been recognized both by the peers, with 2400+ citations to his publications and the advertisement in the mainstream media several times.

Interest
Topics
Details

Details

014
Publications

2021

Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics

Authors
Pinto, JR; Correia, MV; Cardoso, JS;

Publication
IEEE Transactions on Biometrics, Behavior, and Identity Science

Abstract

2021

A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Authors
Castro, HF; Cardoso, JS; Andrade, MT;

Publication
Data

Abstract
The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

2021

ECG Biometrics

Authors
Pinto, JR; Cardoso, JS;

Publication
Encyclopedia of Cryptography, Security and Privacy

Abstract

2021

Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process

Authors
Saffari, M; Khodayar, M; Saadabadi, MSE; Sequeira, AF; Cardoso, JS;

Publication
Sensors

Abstract
In recent years, deep neural networks have shown significant progress in computer vision due to their large generalization capacity; however, the overfitting problem ubiquitously threatens the learning process of these highly nonlinear architectures. Dropout is a recent solution to mitigate overfitting that has witnessed significant success in various classification applications. Recently, many efforts have been made to improve the Standard dropout using an unsupervised merit-based semantic selection of neurons in the latent space. However, these studies do not consider the task-relevant information quality and quantity and the diversity of the latent kernels. To solve the challenge of dropping less informative neurons in deep learning, we propose an efficient end-to-end dropout algorithm that selects the most informative neurons with the highest correlation with the target output considering the sparsity in its selection procedure. First, to promote activation diversity, we devise an approach to select the most diverse set of neurons by making use of determinantal point process (DPP) sampling. Furthermore, to incorporate task specificity into deep latent features, a mutual information (MI)-based merit function is developed. Leveraging the proposed MI with DPP sampling, we introduce the novel DPPMI dropout that adaptively adjusts the retention rate of neurons based on their contribution to the neural network task. Empirical studies on real-world classification benchmarks including, MNIST, SVHN, CIFAR10, CIFAR100, demonstrate the superiority of our proposed method over recent state-of-the-art dropout algorithms in the literature.

2021

Mixture-Based Open World Face Recognition

Authors
Matta, A; Pinto, JR; Cardoso, JS;

Publication
Advances in Intelligent Systems and Computing - Trends and Applications in Information Systems and Technologies

Abstract

Supervised
thesis

2020

Adversarial Domain Adaptation for Sensor Networks

Author
Francisco Tuna de Andrade

Institution
UP-FEUP

2020

A Deep Learning-based Radio-Pathomics Approach for Breast Tumor Signature

Author
Sara Isabel Pires de Oliveira

Institution
INESCTEC

2020

Driver Drowsiness Detection Using Non-Intrusive Eletrocardiogram and Steering Wheel Angle Signals

Author
Margarida João Castro Neves Fernandes

Institution
UP-FEUP

2020

Multimodal Cervical Cancer Diagnosis: Deep Learning for Automatic Decision Support

Author
Tomé Mendes Albuquerque

Institution
UP-FEUP

2020

Performance Anomaly Detection in 802.11 Wireless Networks Applying Hidden Markov Models

Author
Anisa Allahdadidastjerdi

Institution
UP-FCUP