Finger-vein Technology Research Articles

Security of IoT Automated Devices Using Different Authentication Methods

Internet of Things (IoT) has brought significant change to the real world and has impacted every aspect of our lives. The acceptance by users of IoT is extremely significant and its widespread use is due to the availability of autonomous devices. A wide acceptance of IoT in applications in every field is constantly gathering sensitive data and providing an increased surface for hackers. Therefore, privacy-preserving authentication and access control are major issues in the field of research. Methods/Statistical Analysis This paper will look at the ways to secure the authentication of sensors-enabled autonomous devices in the IoT. Additionally, for the purpose of ensuring confidentiality during communications, there are a variety of Authentication methods that meet the requirements of Security. Results: Insecure authentication process biometrics, PINs, and other methods offer additional security for IoT that is autonomously implemented. Also, Finger vein technology is one part that IoT could have a significant impact in protecting their devices. 3D Face detection is the most advanced form of 2D face detection. It enhances the processing and processing speed of image capture.

Development of a Smart Cabinet Lock Based on Finger Vein Technology

In response to the requirements for the security and recognition accuracy of cabinet locks in communications, electric power, energy, chemical, IDC computer rooms, daily homes, and other occasions, a smart cabinet lock with multiple door-opening methods based on finger vein biometric technology is developed. The main method to unlock the cabinet is via finger vein unlocking; that is, the identification and verification of information by analyzing the pattern of the veins on the finger, and the achievement of identity verification and identification through biological detection. At the same time, it supports IC or ID card unlocking and mechanical manual unlocking. In order to simplify the overall structure, the internal mechanical structures of the aforementioned three unlocking methods are simplified into one. Its design focuses on the hybrid mechanical transmission structure and the finger vein module as well as utilizes the functions of each module. The principle is divided into blocks. Through processing and purchasing, the smart cabinet lock device is assembled, and its initial functional requirements are tested with physical objects. From the test results, it can be appreciated that the structural size, unlocking method, and two-way unlocking function of the smart cabinet lock meet the requirements, and its recognition accuracy is high, with good security and certain practical application prospects.

Finger Vein Recognition System Based on Convolutional Neural Network and Android

With the rapid development of science and technology, biotechnology has developed rapidly. Among the many biometric technologies, finger vein technology has the characteristics of vitality, portability, and non-replicability, so it is considered to be the most promising biometric technology. However, the accuracy of finger vein recognition is affected by the collection device, the surrounding temperature and the algorithm. The flaws cannot be applied to real life on a large scale. This paper designs a finger vein recognition system based on convolutional neural network and Android, which mainly includes the following three parts. First, the system hardware includes the design of the acquisition device, the selection of the core development board and the display screen. Second, the design of the entire system software architecture is based on the MVVM architecture, which ensures low coupling of the program and is easy for later expansion and maintenance. The software includes collection function, recognition function and administrator function. Finally, a lightweight neural network is proposed for finger vein feature extraction, and proposed a storage method based on MMKV to meet the real-time performance of the system.

Feature extraction of finger-vein patterns based on boosting evolutionary algorithm and its application for loT identity and access management

With billions of devices being connected, how to make sure that our information stays secure is becoming a hot topic of IoT. Traditional approaches to personal authentication are inadequate and ineffective in the IoT era. Finger vein technology is the current biometric system that utilizes the vein structure for recognition. As such patterns are veiled under the skin surface, they have significant privacy protection and are therefore incredibly difficult to forge. Finger vein recognition has gained a great deal of publicity because earlier approaches experienced significant pitfalls, such as its inability to handle the imbalanced collection of finger veins samples and the detection of distinguishing features in low-quality images. Such disadvantages have triggered a lack of consistency of the optimization algorithm or have contributed to a decrease in its efficiency. The key objective of the research discussed in this paper is to examine the impact of the genetic algorithm in the selection of the optimum vector characteristics of the finger vein. This is done by incorporating a Niching model in the form of a Context-Based Clearing (CBC) procedure to increase the heterogeneity of the features within the features’ vector, with the goal of minimizing the association between them. It also offers the idea of a reduction of the feature set to reduce duplication without reducing accuracy. The performance study of the proposed model is carried out through multiple tests and the findings indicate an overall increase of 6% in the accuracy relative to some of the state-of-the-art finger vein recognition systems present in the literature.

A Study on the Authentication and Security of Financial Settlement Using the Finger Vein Technology in Wireless Internet Environment

The development of the wireless communication allows all of the information to be saved in the digital storage device rapidly. Due to this, hacking and information leakage incidents are rapidly increasing. The scale of the problem however has gradually increased and the targeted industries have become much more diverse, which further points to the severity of the issue. Consequently, there are efforts to develop a security system in order to protect the information, yet at the same time the hacking technology has also advanced, causing an astronomical damage at an increasing state. This has led to the demand for a more convenient and cutting-edge enhanced security solutions. This demand has birthed the security authentication technology which merges biometrics and ICT capabilities. However, numerous biometrics technologies carry problems when deployed as means of security authentication solution for financial services due to their low level of recognition success rate, easy duplication, avoid recognition, terminal minimization difficulties and more. Finger vein recognition technology which is impossible to duplicate with a very high level of recognition rate has emerged as the biometrics authentication solution for financial services. This study recommended an authentication security model for financial services that use finger vein solution to strengthen financial services' safety and to protect information.

Finger Vein Detection using Gabor Filter, Segmentation and Matched Filter

This paper propose a method of personal identification based on finger-vein patterns. An image of a finger captured by the web camera under the IR light transmission contains not only the vein pattern itself; but also shade produced by various thickness of the finger muscles; bones; and tissue networks surrounding the vein. In this paper; we intro-duce preliminary process to enhance the image quality worsened by light effect and noise produced by the web camera; then segment the vein pattern by using adaptive threshold method and matched them using improved template matching. The main purposes of this paper are to investigate finger-vein technology; the applicable fields and whether finger-vein detection can solve the problems fingerprint detection imposes in certain industries.

ITCard connects Polish banks with fingervein biometrics

ITCard SA, a major provider of banking services in Poland, has launched a biometric ATM shared network in Europe called Planet Cash. By the end of 2014 the company expects to have implemented a network of around 2000 biometric ATMs using Hitachi Europe's fingervein technology.

User identification based on finger-vein patterns for consumer electronics devices

With the development of consumer electronics technologies, many more powerful consumer devices such as cell phone, PDA and laptop have brought consumers an unprecedented level of convenience and flexibility. However, how to protect personal private information stored in the consumer electronics devices from misuses owing to theft or loss is becoming a major issue. To solve the problem, more secure and reliable user identification mechanisms using biometrics technology should be equipped into the consumer electronics devices. This paper presents a user identification system framework using finger-vein technology for consumer electronics devices. The finger-vein identification system is one of the biometrics sensor technologies, which provides high security and reliability than other identification technology. The algorithm of the proposed system compose a feature extraction using Radon transform and singular value decomposition (SVD) and classification using a normalized distance measure. The experimental results indicate that the proposed system performs well for user identification and achieves good performance in terms of the false acceptance rate (FAR) and the false rejection rate (FRR).

Driver identification using finger-vein patterns with Radon transform and neural network

A driver identification system using finger-vein technology and an artificial neural network is presented in this paper. The principle of the proposed system is based on the function of near infra-red finger-vein patterns for biometric authentication. Finger-vein patterns are required by transmitting near infra-red through a finger and capturing the image with an infra-red CCD camera. The algorithm of the proposed system consists of a combination of feature extraction using Radon transform and classification using the neural network technique. The Radon transform can concentrate the information of an image in a few high-valued coefficients in the transformed domain. The neural networks are used to develop the training and testing modules. The artificial neural network techniques using radial basis function network and probabilistic neural network are proposed to develop a driver identification system. The experimental results indicated the proposed system performs well for personal identification. The average identification rate of PNN network is over 99.2%. The details of the image processing technique and the characteristic of system are also described in this paper.