Comparative Study of Deep Learning based Finger Vein Biometric Authentication Systems

Abstract

Over the years Biometric authentication system has gained widespread popularity due to the rising need for personal identification and security. The most common biometric features used in authentication systems are fingerprint, iris, hand geometry, retina, finger vein, palm vein, and voice pattern. Among them, finger vein biometric recognition system might soon surge ahead for its many advantages, such as- high identification accuracy, non-invasive technology, and little to no possibility of being duplicated or forged. After being motivated by the apparent benefits of finger vein authentication, many researchers have tried to develop a working model with improved performance. Since, deep learning has the ability to solve complex problems that require discovering hidden patterns in structured data and understanding intricate relationships between a large number of interdependent variables, many deep learning models were created for feature extraction and classification purposes. In this paper, existing research works of finger vein authentication based on deep learning models have been assembled and summarized. In addition, the accuracy and performance of the approaches have been highlighted to give a future direction for further research.