A New Enhancement Edge Detection of Finger-Vein Identification for Carputer System

Abstract

Developments in multimedia and mobile communication technologies and in mobilized, personalized information security has benefitted various sectors of society, as traditional identification technologies are often complicated. In response to the sharing economy and the intellectualization of automotive electronics, major automobile companies are using biometric recognition to enhance the safety, uniqueness, and convenience of their vehicles. This study uses a deep learning-based finger-vein identification system for carputer systems. The proposed enhancement edge detection adapts to the detected fingers’ rotational and translational movements and to interference from external light and other environmental factors. This study also determines the effect of preprocessing methods on the system’s effectiveness. The experimental results demonstrate that the proposed system allows more accurate identification of 99.1% and 98.1% in various environments, using the FV-USM and SDUMLA-HMT public datasets. As results, the contribution of system is high accuracy and stability for more sanitary, contactless applications makes it eminently suited for use during the COVID-19 pandemic.

Keywords

• biometric features
• finger-vein
• convolutional neuron networks
• carputer