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Sobre

Ana F. Sequeira é licenciada em Matemática, desde 2002, Mestre em Engenharia Matemática, desde 2007, pela Faculdade de Ciências e doutorada em Engenharia e Eletrotécnica e de Computadores, desde 2015, pela Faculdade de Engenharia, ambas as faculdades da Universidade do Porto.

Ana F. Sequeira colaborou com o INESC TEC como investigadora durante o seu doutoramento que visou as áreas de visão computacional e "machine learning" com foco em metodologias de detecção de vivacidade em íris e impressão digital.

Após a conclusão do doutoramento, Ana F. Sequeira colaborou na Universidade de Reading, UK, em dois projectos europeus relacionados com a aplicação de reconhecimento biométrico em controlo de fronteiras (FASTPASS e PROTECT).

A esta actividade seguiu-se uma colaboração a curto-prazo com a empresa Irisguard UK com o objectivo de pesquisar vulnerabilidades do produto EyePay® e desenvolver um protótipo de uma medida de protecção contra “spoofing attacks”.

Actualmente, Ana F. Sequeira colabora novamente com o INESC TEC como investigadora contratado.

Enquanto doutoranda e pós-doc, desde 2011, Ana F. Sequeira é coautora de vários artigos incluindo conferencias internacionais e revistas reconhecidas pela comunidade por citações; assim como liderou a criação de bases de dados e organização de eventos como competições e eventos.

Ao longo da sua  actividade de investigação Ana F. Sequeira adquiriu vasta experiência não apenas em tópicos de visão computacional/processamento de imagem mas também na aplicação de técnicas diversificadas de “machine learning”, desde as metodologias clássicas até as de “deep learning”.

Tópicos
de interesse
Detalhes

Detalhes

001
Publicações

2020

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

Autores
Sequeira, AF; Silva, W; Pinto, JR; Gonçalves, T; Cardoso, JS;

Publicação
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.

2019

Adversarial learning for a robust iris presentation attack detection method against unseen attack presentations

Autores
Ferreira, PM; Sequeira, AF; Pernes, D; Rebelo, A; Cardoso, JS;

Publicação
2019 International Conference of the Biometrics Special Interest Group, BIOSIG 2019 - Proceedings

Abstract
Despite the high performance of current presentation attack detection (PAD) methods, the robustness to unseen attacks is still an under addressed challenge. This work approaches the problem by enforcing the learning of the bona fide presentations while making the model less dependent on the presentation attack instrument species (PAIS). The proposed model comprises an encoder, mapping from input features to latent representations, and two classifiers operating on these underlying representations: (i) the task-classifier, for predicting the class labels (as bona fide or attack); and (ii) the species-classifier, for predicting the PAIS. In the learning stage, the encoder is trained to help the task-classifier while trying to fool the species-classifier. Plus, an additional training objective enforcing the similarity of the latent distributions of different species is added leading to a 'PAI-species'-independent model. The experimental results demonstrated that the proposed regularisation strategies equipped the neural network with increased PAD robustness. The adversarial model obtained better loss and accuracy as well as improved error rates in the detection of attack and bona fide presentations. © 2019 Gesellschaft fuer Informatik.

2018

Robust Clustering-based Segmentation Methods for Fingerprint Recognition

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

Publicação
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.

2018

Mobile NIR Iris Recognition: Identifying Problems and Solutions

Autores
Hofbauer, H; Jalilian, E; Sequeira, AF; Ferryman, J; Uhl, A;

Publicação
2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

Abstract

2018

PROTECT Multimodal DB: Fusion evaluation on a novel multimodal biometrics dataset envisaging Border Control

Autores
Sequeira, AF; Chen, L; Ferryman, J; Galdi, C; Chiesa, V; Dugelay, JL; Maik, P; Gmitrowicz, P; Szklarski, L; Prommegger, B; Kauba, C; Kirchgasser, S; Uhl, A; Grudzie, A; Kowalski, M;

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

Abstract
This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible iris, finger and hand veins, voice and anthropometrics. This dataset will constitute a valuable resource to the field with its number and variety of biometric traits. Acquired in the context of the EU PROTECT project, the dataset allows several combinations of biometric traits and envisages applications such as border control. Based upon the results of the unimodal data, a fusion scheme was applied to ascertain the recognition potential of combining these biometric traits in a multimodal approach. Due to the variability on the discriminative power of the traits, a leave the n-best out fusion technique was applied to obtain different recognition results. © 2018 Gesellschaft fuer Informatik.