Machine Learning Based Impostor Detection By Invariant Features From Nir Finger Vein Imaging

Abstract

Biometrics mostly used as personal identification accordingly securing a client against the unapproved utilization of his or her identity. Acquiring biometric information is getting to be simpler. Smartphones and other advanced technologies exist from which biometric data can collect easily without the knowledge of others. Finger vein authentication is a method for biometric verification that depends on a vein pattern, which is located beneath the human finger’s skin. Veins are covered with skin that cannot be copied by others. In this research, our focus is on these invariant features of finger veins. We have collected invariant features from various recent features extraction techniques and then classified with state-of-the-art machine learning classifiers. For this purpose, we have used publicly available finger vein image databases. The performance has been evaluated by various evaluation metrics and comparative analysis of various machine learning classifiers has been presented to describe the performance of these classifiers on the said data set.