Can I read Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach on EtoBox?

Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach by Adami, Banafsheh; Karimian, Nima is a scholarly article available to read on EtoBox.

What is Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach about?

Contactless fingerprint recognition offers a higher level of user comfort and addresses hygiene concerns more effectively. However, it is also more vulnerable to presentation attacks such as photo paper, paper-printout, and various display attacks, which makes it more challenging to implement in biometric systems compared to contact-based modalities. Limited research has been conducted on presentation attacks in contactless fingerprint systems, and these studies have encountered challenges in terms of generalization and scalability since both bonafide samples and presentation attacks are utilized during training model. Although this approach appears promising, it lacks the ability to handle unseen attacks, which is a crucial factor for developing PAD methods that can generalize effectively. We introduced an innovative anti-spoofing approach that combines an unsupervised autoencoder with a convolutional block attention module to address the limitations of existing methods. Our model is exclusively trained on bonafide images without exposure to any spoofed samples during the training phase. It is then evaluated against various types of presentation attack images in the testing phase.

Author

Adami, Banafsheh; Karimian, Nima

Published

2023

Language

EN