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About

Ana F. Sequeira holds a PhD in Electrical and Computing Engineering obtained from the Engineering Faculty of University of Porto, Portugal in 2015. Ana also holds a Master degree in Mathematical Engineering and a 5-years degree in Mathematics, both obtained from the Mathematics Department of the Science Faculty of the University Of Porto, Portugal.

Ana collaborated as a researcher at INESC TEC, a R&D institute affiliated to the University of Porto, within the Visual Computing and Machine Intelligence Group (VCMI) during her PhD studies.

Ana’s PhD studies, in the fields of computer vision and machine learning, focused on liveness detection techniques for iris and fingerprint. This research equipped Ana with a deep knowledge and diversified skills regarding the complete image processing and classification pipeline: from the pre-processing methods to the classification/decision step passing through the application of feature extraction techniques.

The post-doctoral research was pursued at the University of Reading, UK, collaborating in EU projects related to the application of biometric recognition in Border Control (FASTPASS and PROTECT projects).

This activity was followed by a short term collaboration with the company Iris Guard UK in order to research on the vulnerabilities of EyePay® technology’s to spoofing and to develop a proof-of-concept of an anti-spoofing measure.

Currently, Ana is back at INESC TEC as a Research Assistant.

During Ana’s activity as PhD and PDRA, she authored and co-authored several research publications in major international conferences and journals which attracted, to the date, over 150 citations.

Throughout her research activity, Ana developed expertise not only in computer vision/image processing topics but as well in the application of diversified machine learning techniques, from classic to deep learning methodologies.

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Publications

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.

2020

Interpretable Biometrics: Should We Rethink How Presentation Attack Detection is Evaluated?

Authors
Sequeira, AF; Silva, W; Pinto, JR; Goncalves, T; Cardoso, JS;

Publication
8th International Workshop on Biometrics and Forensics, IWBF 2020, Porto, Portugal, April 29-30, 2020

Abstract
Presentation attack detection (PAD) methods are commonly evaluated using metrics based on the predicted labels. This is a limitation, especially for more elusive methods based on deep learning which can freely learn the most suitable features. Though often being more accurate, these models operate as complex black boxes which makes the inner processes that sustain their predictions still baffling. Interpretability tools are now being used to delve deeper into the operation of machine learning methods, especially artificial networks, to better understand how they reach their decisions. In this paper, we make a case for the integration of interpretability tools in the evaluation of PAD. A simple model for face PAD, based on convolutional neural networks, was implemented and evaluated using both traditional metrics (APCER, BPCER and EER) and interpretability tools (Grad-CAM), using data from the ROSE Youtu video collection. The results show that interpretability tools can capture more completely the intricate behavior of the implemented model, and enable the identification of certain properties that should be verified by a PAD method that is robust, coherent, meaningful, and can adequately generalize to unseen data and attacks. One can conclude that, with further efforts devoted towards higher objectivity in interpretability, this can be the key to obtain deeper and more thorough PAD performance evaluation setups. © 2020 IEEE.

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.).

2020

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study

Authors
Galdi, C; Boyle, J; Chen, LL; Chiesa, V; Debiasi, L; Dugelay, JL; Ferryman, J; Grudzien, A; Kauba, C; Kirchgasser, S; Kowalski, M; Linortner, M; Maik, P; Michon, K; Patino, L; Prommegger, B; Sequeira, AF; Szklarski, L; Uhl, A;

Publication
IET BIOMETRICS

Abstract
Pervasive and useR fOcused biomeTrics bordEr projeCT (PROTECT) is an EU project funded by the Horizon 2020 research and Innovation Programme. The main aim of PROTECT was to build an advanced biometric-based person identification system that works robustly across a range of border crossing types and that has strong user-centric features. This work presents the case study of the multibiometric verification system developed within PROTECT. The system has been developed to be suitable for different borders such as air, sea, and land borders. The system covers two use cases: the walk-through scenario, in which the traveller is on foot; the drive-through scenario, in which the traveller is in a vehicle. Each deployment includes a different set of biometric traits and this study illustrates how to evaluate such multibiometric system in accordance with international standards and, in particular, how to overcome practical problems that may be encountered when dealing with multibiometric evaluation, such as different score distributions and missing scores.

2020

Multimedia systems and applications in biomedicine

Authors
Domingues, I; Sequeira, AF; Pinto, C; Rocha,;

Publication
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION

Abstract

Supervised
thesis

2020

Fingerprint Anti Spoofing – Domain Adaptation and Adversarial Learning

Author
João Afonso Pinto Pereira

Institution
UP-FEUP

2020

Head Pose Estimation for Biometric Recognition Systems

Author
João Manuel Guedes Ferreira

Institution
UP-FEUP

2020

Explainable Artificial Intelligence For Biometric Analysis

Author
Pedro Carneiro Neto

Institution
UP-FEUP

2020

Face biOmetrics UNder severe representation Drifts

Author
Mohsen Saffari

Institution
UP-FEUP