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Sobre

Jaime S. Cardoso, licenciado em Engenharia e Eletrotécnica e de Computadores em 1999, Mestre em Engenharia Matemática em 2005 e doutorado em Visão Computacional em 2006, todos pela Universidade do Porto. Professor Associado com agregação na Faculdade de Engenharia da Universidade do Porto (FEUP) e Investigador Sénior em 'Information Processing and Pattern Recognition' no Centro de Telecomunicações e Multimédia do INESC TEC.

A sua investigação assenta em três grandes domínios: visão computacional, "machine learning" e sistemas de suporte à decisão. A investigação em processamento de imagem e vídeo tem abordado a área de biometria, imagem médica e "video tracking" para aplicações de vigilância e desportos. O trabalho em "machine learning" foca-se na adaptação de sistemas de aprendizagem às condições desafiantes de informação visual. A ênfase dos sistemas de suporte à decisão tem sido dirigida a aplicações médicas, sempre ancoradas com a análise automática de informação visual.

É co-autor de mais de 150 artigos, dos quais mais de 50 em jornais internacionais, com mais de 2400 citações (google scholar). Foi investigador principal em 6 projectos de I&D e participou em 14 projectos de I&D, incluindo 5 projectos europeus e um contrato directo com a BBC do Reino Unido.

Tópicos
de interesse
Detalhes

Detalhes

014
Publicações

2021

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

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

Publicação
IEEE Transactions on Biometrics, Behavior, and Identity Science

Abstract

2021

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

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

Publicação
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

Autores
Pinto, JR; Cardoso, JS;

Publicação
Encyclopedia of Cryptography, Security and Privacy

Abstract

2021

Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process

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

Publicação
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

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

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

Abstract

Teses
supervisionadas

2020

Open World Face Recognition

Autor
Arthur Johas Matta

Instituição
UP-FEUP

2020

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Autor
João Tiago Ribeiro Pinto

Instituição
UP-FEUP

2020

Aquaculture fish quality control using synthetic data

Autor
Hugo Miguel Miranda Barros

Instituição
UP-FEUP

2020

Automotive Interior Sensing - Temporal Consistent Human Body Pose Estimation

Autor
José Martinho Oliveira Peres

Instituição
UP-FEUP

2020

Reduction of non-regression time through Artificial Intelligence

Autor
Mariana Lopes da Silva

Instituição
UP-FEUP