A Biometric System Design using Finger Knuckle Biological Trait

Various biometric traits are available but recently finger knuckle image has attracted great attention to biometric research community due to its potentiality and ease of use. The performance of any biometric system heavily depends on the accuracy of feature detection and robustness of feature description. A number of feature descriptors are available but selection is being determined by the type of application such as image retrieval, biometric system, remote sensing etc. This paper proposed a biometric system using leading descriptor for finger knuckle biological trait image recognition and also compare the proposed system with existing leading state-of-art finger knuckle print recognition. The recognition performance is measured by some standard evaluation protocol such as Equal Error Rate (EER), Decidability index, Computation cost, Zero-score imposter probability, Zero-score genuine probability, Receiver Operating Characteristic (ROC), Detection Error Trade-off (DET) over PolyU Finger Knuckle benchmark database. The experimental results show that, the performance of SURF, KAZE and ORB are comparable and are better as compared to BRISK and MSER descriptor. The ERR of 0.0010% is obtained with ORB descriptor while the Decidability index of 6.4645 is obtained for KAZE. The minimum Computational cost of 0.1442 s is obtained for SURF as compares to other of its class.

Automated summary

This study proposed a biometric system using leading descriptor for finger knuckle biological trait image recognition and compared it with existing leading state-of-art finger knuckle print recognition. Experimental results showed that the performance of SURF, KAZE, and ORB descriptors are comparable and better than BRISK and MSER descriptors.