A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric
  Systems
Banafsheh Adami Sara Tehranipoor Nasser Nasrabadi Nima Karimian
With the increasing integration of smartphones into our daily lives,
fingerphotos are becoming a potential contactless authentication method. While
it offers convenience, it is also more vulnerable to spoofing using various
presentation attack instruments (PAI). The contactless fingerprint is an
emerging biometric authentication but has not yet been heavily investigated for
anti-spoofing. While existing anti-spoofing approaches demonstrated fair
results, they have encountered challenges in terms of universality and
scalability to detect any unseen/unknown spoofed samples. To address this
issue, we propose a universal presentation attack detection method for
contactless fingerprints, despite having limited knowledge of presentation
attack samples. We generated synthetic contactless fingerprints using StyleGAN
from live finger photos and integrating them to train a semi-supervised
ResNet-18 model. A novel joint loss function, combining the Arcface and Center
loss, is introduced with a regularization to balance between the two loss
functions and minimize the variations within the live samples while enhancing
the inter-class variations between the deepfake and live samples. We also
conducted a comprehensive comparison of different regularizations' impact on
the joint loss function for presentation attack detection (PAD) and explored
the performance of a modified ResNet-18 architecture with different activation
functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center
loss. Finally, we evaluate the performance of the model using unseen types of
spoof attacks and live data. Our proposed method achieves a Bona Fide
Classification Error Rate (BPCER) of 0.12\%, an Attack Presentation
Classification Error Rate (APCER) of 0.63\%, and an Average Classification
Error Rate (ACER) of 0.37\%.