Finger Knuckleprint Based Personal Authentication Using Siamese Network

Online security is a major concern today and incidents of forged identity cards and hacked passwords are common throughout the world. Therefore, there is a need for robust personal authentication mechanisms using biometrics for various access control systems. Popular biometric traits such as fingerprint have problems in rural areas, due to wearing down of fingerprint pattern from hard manual labor. This is also a problem for people who work with calcium oxide, because it is known to dissolve the upper layers of the skin due to its basicity. This paper proposes a finger-knuckle-print (FKP) based human authentication system that is immune to the above problems because the finger dorsal region is not exposed to labor surfaces. The paper uses pre-processed knuckle ROI images to train a Siamese convolutional neural network model. The proposed algorithm has been validated using open-source PolyU finger-knuckle-print database from 165 individuals, and has achieved 99.24% CRR, 0.78% EER that is better than the state-of-the-art.