# A New Enhancement Edge Detection of Finger-Vein Identification for Computer System
## 1 Introduction  
Artificial intelligence (AI), the Internet of things (IoT), bigdata, mobile networks (MN) and cloud computing (CC) have been advancing rapidly. The growth of autonomous vehicles (AVs) and the Internet of Vehicles (IoV) means that these technologies are increasingly integrated into people's lives. However, consumers who do not often drive find automobiles to be too costly. Costs include a high purchasing price, depreciation, insurance, maintenance, taxes and various other fees during and after the purchase of an automobile. A sharing economy would address this issue. The sharing economy business model promotes the redistribution of unused automobiles by carsharing, changing the traditional business model by providing reciprocal benefits for both the sharer and the user. This economic structure allows the sharing of automobiles as an alternative to the greater economic burden of individual purchasing. Most carsharing platforms identify users using independently developed applications (App), which involves a registration process and the submission of an identity card, a driver's license, or other documents to verify identity and driver qualifications. These documents must be verified before users rent a vehicle. Vehicles can be unlocked using virtual keys but logging into the app requires a traditional account and password verification. This identification process for carsharing platforms has numerous flaws: if an account were to be compromised, it is impossible to determine whether the driver is the registered user. Automobiles can be unlocked using a key or a keyless entry system. Physical keys can be replicated using pictures or molding techniques, but keyless entry systems are moresecure. However, keyless entry systems unlock automobiles using near-field communication (NFC) [1] or Bluetooth [2] technology but a Bluetooth connection is unstable and media access addresses (MAC) are also susceptible to replication, so security breaches are possible. NFC technology is more stable and safer than a Bluetooth connection, but it is also easily replicable. To address the security flaws of traditional identification documents, passwords, key and keyless entry systems, high-end automobiles use biometric authentication to increase security. As the demand for privacy, security and convenience increases, biometrics have become increasingly popular as a fast and simple identification process that offers security, reliability, and accuracy. Its convenience also allows for safe monitoring and system management and integration [3,4]. Biometric characteristics are universal, permanent, distinctive and collectable [5, 6, 7]. Biometrics are more secure than traditional identification methods because keys or ID cards are not required. The physiological characteristics that biometrics detect comprise external characteristics and internal characteristics. External characteristics include traits such as a fingerprint, a face, and a palm-print and internal characteristics include traits such as finger-veins and palm-vein. Each of these characteristics are used for biometric analysis but internal characteristics allow more accurate and secure identification. Internal characteristics that involve vessels such as finger-veins beneath the skin are not prone to wear or interference from external staining, water, light or other environmental variables that undermine identification [3,4]. External characteristics are more easily counterfeited and affected by an external environment, so they are less suited to biometrics. Internal vein textures do not change significantly over time. Even the veins of twins have different texture to allow for identification [5]. Vein imaging uses myoglobin's absorbance of infrared light to capture images using infrared (NIR) and visible light (RGB) detectors. Blood does not flow in deceased humans so finger-vein textures can only be acquired from living bodies [5,6]. Finger-vein identification systems use 1 to 1 verification and 1 to \(N\) recognition systems.