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

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Pedro M. Ferreira (Author) Other; Ana F. Sequeira (Author) Other; Diogo Pernes (Author) FEUP; Ana Rebelo (Author) Other; Jaime S. Cardoso (Author) FEUP

Conference proceedings International

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

Pages: 47-58

2019 International Conference of the Biometrics Special Interest Group, BIOSIG 2019

18 September 2019 through 20 September 2019

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 47 Citations

Other information

Authenticus ID: P-00R-F8M

Abstract (EN): 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 'PAIspecies'- 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 fur Informatik (GI). All rights reserved.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7