On-the-Fly Finger-Vein-Based Biometric Recognition Using Deep Neural Networks

Abstract

Finger-vein-based biometric recognition technology has recently attracted the attention of both academia and industry because of its robustness against presentation attacks and the convenience of the acquisition process. As a matter of fact, some contactless vein-based recognition systems have already been deployed and commercialized. However, they require the users to keep their hands still over the acquisition device for a few seconds to perform recognition. In this study, we release this constraint and allow users to have their finger vein patterns acquired on-the-fly. To accomplish this goal, we introduce an ad-hoc acquisition architecture capable of capturing the finger vein structure using an array of low-cost cameras, and we propose a recognition framework based on the use of convolutional and recurrent neural networks. To test the proposed approach we acquire a finger vein image dataset, in video format at four different exposure times, from 100 subjects. The obtained experimental results show that, even in a very challenging scenario, the proposed system guarantees high performance levels, up to 99.13% recognition accuracy over the collected dataset.