Abstract:
Fingerprint recognition systems have shown some weaknesses
related to security issues such as fake presentation attacks. Therefore, it is
necessary to protect these systems against attacks by incorporating fingerprint
liveness detection (FLD) algorithms that must be able to distinguish between
live and fake fingerprints. In this work, we propose a deep learning framework
for FLD. Specifically, deep neural networks based transfer learning is proposed
to classify the fingerprint as live or fake fingerprint. The proposed method
focuses on the important features of fingerprint, which allows enhancing the
feature representation and suppresses the less relevant ones. We evaluated the
proposed FLD on LivDet2023 database, and results, meticulously analyzed,
unveil the superior performance of the proposed method. Notably, the proposed
method with DensNet201 model attains an exceptional accuracy of 98.36% on
the LivDet2023 dataset, surpassing other deep networks
