  Present world has already been consistently exploring the fine edges of
online and digital world by imposing multiple challenging problems/scenarios.
Similar to physical world, personal identity management is very crucial
in-order to provide any secure online system. Last decade has seen a lot of
work in this area using biometrics such as face, fingerprint, iris etc. Still
there exist several vulnerabilities and one should have to address the problem
of compromised biometrics much more seriously, since they cannot be modified
easily once compromised. In this work, we have proposed a secure cancelable
finger dorsal template generation network (learning domain specific features)
secured via. Bio-Hashing. Proposed system effectively protects the original
finger dorsal images by withdrawing compromised template and reassigning the
new one. A novel Finger-Dorsal Feature Extraction Net (FDFNet) has been
proposed for extracting the discriminative features. This network is
exclusively trained on trait specific features without using any kind of
pre-trained architecture. Later Bio-Hashing, a technique based on assigning a
tokenized random number to each user, has been used to hash the features
extracted from FDFNet. To test the performance of the proposed architecture, we
have tested it over two benchmark public finger knuckle datasets: PolyU FKP and
PolyU Contactless FKI. The experimental results shows the effectiveness of the
proposed system in terms of security and accuracy.
