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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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001
Publications

2019

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

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

Publication
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

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.

2018

Mobile NIR Iris Recognition: Identifying Problems and Solutions

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

Publication
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

Authors
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;

Publication
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.

2017

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Authors
Sequeira, AF; Chen, L; Ferryman, J; Wild, P; Alonso-Fernandez, F; Bigun, J; Raja, KB; Raghavendra, R; Busch, C; de Freitas Pereira, T; Marcel, S; Behera, SS; Gour, M; Kanhangad, V;

Publication
2017 IEEE International Joint Conference on Biometrics (IJCB)

Abstract