# A New Enhancement Edge Detection of Finger-Vein Identification for Computer System
## 2 Review of Literature  
Finger-vein biometrics use statistical computations such as local binary histograms (LBH) [8], local derivative patterns (LDP) [9] and local line binary patterns (LLBP) [10] to analyze images and compare similarities between vein textures. However, these computational methods are easily affected by interference from external light sources or the rotational and translational displacement of the user's fingers, which decreases identification accuracy. A previous study [5] and the study by [PERSON] et al. [11] propose algorithms that use vein textures. These algorithms identify the darker shades of veins on the grayscale of digital images and track them to identify the texture. In [6], mathematically converted identified features to allow for correcting the errors due to rotational and translational finger displacements, but interference from external light sources cannot be countered. External light sources affect an image's brightness and prevent the capture of vein features, so the identification rate decreases. [PERSON] et al. [12] used bicubic interpolation to decrease the spatial resolution and create a more lightweight system but this approach inhibits the acquisition of vein texture data, so identification accuracy is decreased. Previous studies proposed finger-vein recognition using tri-branch feature points [5,13], but unstable light sources decrease the number of identifiable tri-branch feature points, so there are errors. However, these algorithms require significant computational power, so response times are slow. As CPU and GPU computing power increases and costs decrease, identification algorithms use deep learning (DL) to improve accuracy by processing and learning from large data sets of images, so identification using biometrics is more accurate. Other studies use a convolutional neural network (CNN) to extract and categorize texture features for finger-vein biometrics. CNNs are highly effective but a previous study [3,4] proposed an identification process that is slower because the system must process more samples for DL. These recognition technologies all possess flaws. Traditional image algorithms have lower error rates for specific equipment and illumination conditions but when these settings are altered, the system's recognition effectiveness varies. Image algorithms only use a small portion of all data during experimentation and if the dataset is expanded, the error rate changes. Traditional image algorithms retain the users' vein features they are insufficiently secure to ensure total information safety for financial businesses and government agencies. DL-based algorithms also require large data sets, so they are susceptible to overfitting of finger-vein biometrics. Contemporary shared automobiles verify user identities by verifying traditional ID documents. The registration processes require manual checking of driver licenses and ID documents. After registration, users rent cars by logging in with their account and password. Studies show that traditional identification features numerous risks and informational security weaknesses. Access to a registered carsharing service is possible using an account and password so anyone, including those underaged or those without a driver's license, can use shared automobiles if they have the password. The traditional method is also vulnerable to invalid registrations using fabricated documents. Personal data breaches are possible, so the system does not protect information. To increase information security for carsharing systems, this study uses finger-vein biometric algorithms in shared automobiles' carputers and an enhanced edge detection method to correct for rotational and translational movements of fingers and interference from external light sources and other factors in the environment. The system allows stable, recognition if there is interference from external light sources and complex environments.