A robust fingerprint presentation attack detection method against unseen attacks through adversarial learning

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Pereira, JA (Author) Other; Sequeira, AF (Author) Other; Pernes, D (Author) FEUP; Jaime S Cardoso (Author) FEUP

Conference proceedings International

Title: BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group

Pages: 183-190

19th International Conference of the Biometrics Special Interest Group, BIOSIG 2020

16 September 2020 through 18 September 2020

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 0 Citations

Other information

Authenticus ID: P-00S-ZMP

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5