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

FDFNet : A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing