Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep
  Learning Approach
Banafsheh Adami Nima Karimian
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. The scheme we
proposed has achieved an average BPCER of 0.96\% with an APCER of 1.6\% for
presentation attacks involving various types of spoofed samples.