학술논문

Finger vein Recognition
Document Type
Conference
Source
2022 International Telecommunications Conference (ITC-Egypt) Telecommunications Conference (ITC-Egypt), 2022 International. :1-5 Jul, 2022
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Temperature sensors
Deep learning
Image recognition
Veins
Fingerprint recognition
Feature extraction
Sensor systems
Finger vein
Biometrics
Minutiae
Recognition
Image
Extraction
Language
Abstract
Biometric systems are quickly replacing traditional password authentication solutions. Security and recognition accuracy are the two most important factors to consider when designing a biometric system. Biometric identification is the study of an individual’s physiological and behavioral features to solve security and identification challenges. Fingerprint biometrics have been used for a long time, but finger-vein image identification has lately gained traction as a potential biometric technique. Fingerprints have long been a widely accepted biometric for identification, but their flaws and vulnerability to spoofing prompted the development of finger vein biometrics, which were thought to be safer and more dependable. Finger vein recognition (FVR) is a technology in biometric that analyses the patterns of a person’s finger veins to authenticate their identity. This article evaluates all aspects of fingerprint and finger vein recognition (FVR), including image acquisition, pre-processing, feature extraction, and matching. An overview of fingerprint and finger vein biometric systems has been presented, along with some benefits and drawbacks. For the development of an auto fingerprint and finger vein identification system, it is critical to extract fingerprint minutiae. Deep learning and algorithms can both be utilized to determine the best ways for feature extraction for fingerprints and finger veins, which will improve the fusion to create a multimodal system.