scholarly journals Biometrics Systems and Technologies: A survey

2016 ◽  
Vol 11 (3) ◽  
pp. 315 ◽  
Author(s):  
Ioan Buciu ◽  
Alexandru Gacsadi
Keyword(s):  
Face Recognition ◽  
Human Computer Interaction ◽  
Biometric System ◽  
Interested Reader ◽  
Biometric Security ◽  
Detailed List ◽  
Biometric Templates ◽  
Password Based Authentication ◽  
Biometric Features ◽  
Biometric Based Authentication

In a nutshell, a biometric security system requires a user to provide some biometric features which are then verified against some stored biometric templates. Nowadays, the traditional password based authentication method tends to be replaced by advanced biometrics technologies. Biometric based authentication is becoming increasingly appealing and common for most of the human-computer interaction devices. To give only one recent example, Microsoft augmented its brand new Windows 10 OS version with the capability of supporting face recognition when the user login in. This chapter does not intend to cover a comprehensive and detailed list of biometric techniques. The chapter rather aims at briefly discussing biometric related items, including principles, definitions, biometric modalities and technologies along with their advantages, disadvantages or limitations, and biometric standards, targeting unfamiliar readers. It also mentions the attributes of a biometric system as well as attacks on biometrics. Important reference sources are pointed out so that the interested reader may gain deeper in-depth knowledge by consulting them.

Biometric Security

2015 ◽  
pp. 199-217
Author(s):  
Muzhir Shaban Al-Ani
Keyword(s):  
Biological Sciences ◽  
Data Analysis ◽  
Personal Identification ◽  
Biometric System ◽  
Mathematical Methods ◽  
Security Systems ◽  
Biometric Security ◽  
The Future ◽  
Behavioral Parameters ◽  
Biometric Features

The terms biometrics and biometry have been used to refer to the field of development of statistical and mathematical methods applicable to data analysis problems in the biological sciences. Recently biometrics refers to technologies and applications applied for personal identification using physical and behavioral parameters. Biometric security systems ensuring that only the authorized persons are permitted to access a certain data, because it is difficult to copy the biometric features pattern for a specific person. Biometrics is playing an important role in applications that are centric on identification, verification and classification. This chapter focuses on biometric security in their types, specifications, technologies and algorithms. Some algorithms of biometric security are also included in this chapter. Finally latest and future aspects of biometric system and merging technologies are also mentioned, including more details of system structures and specifications and what constitution will shape biometric security of in the future.

Biometric Security

Biometrics ◽  
2017 ◽  
pp. 1399-1418
Author(s):  
Muzhir Shaban Al-Ani
Keyword(s):  
Biological Sciences ◽  
Data Analysis ◽  
Personal Identification ◽  
Biometric System ◽  
Mathematical Methods ◽  
Security Systems ◽  
Biometric Security ◽  
The Future ◽  
Behavioral Parameters ◽  
Biometric Features

The terms biometrics and biometry have been used to refer to the field of development of statistical and mathematical methods applicable to data analysis problems in the biological sciences. Recently biometrics refers to technologies and applications applied for personal identification using physical and behavioral parameters. Biometric security systems ensuring that only the authorized persons are permitted to access a certain data, because it is difficult to copy the biometric features pattern for a specific person. Biometrics is playing an important role in applications that are centric on identification, verification and classification. This chapter focuses on biometric security in their types, specifications, technologies and algorithms. Some algorithms of biometric security are also included in this chapter. Finally latest and future aspects of biometric system and merging technologies are also mentioned, including more details of system structures and specifications and what constitution will shape biometric security of in the future.

scholarly journals Secure Online Examination with Biometric Authentication and Blockchain-Based Framework

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaoling Zhu ◽  
Chenglong Cao
Keyword(s):  
Access Control ◽  
Biometric Authentication ◽  
Fine Grained ◽  
Fuzzy Vault ◽  
Malicious Behavior ◽  
Online Examination ◽  
E Learning ◽  
Important Means ◽  
Biometric Templates ◽  
Biometric Features

E-learning has been carried out all over the world and then online examinations have become an important means to check learning effect during the outbreak of COVID-19. Participant authenticity, data integrity, and access control are the assurance to online examination. The existing online examination schemes cannot provide the protection of biometric features and fine-grained access control. Particularly, they did not discuss how to resolve some disputes among students, teachers, and a platform in a fair and reasonable way. We propose a novel biometric authentication and blockchain-based online examination scheme. The examination data are encrypted to store in a distributed system, which can be obtained only if the user satisfies decryption policy. And the pieces of evidence are recorded in a blockchain network which is jointly established by some credible institutions. Unlike other examination authentication systems, face templates in our scheme are protected using a fuzzy vault and a cryptographic method. Furthermore, educational administrative department can determine who the real initiator of malicious behavior is when a dispute arises using a dispute determination protocol. Analysis shows that no central authority is required in our scheme; the collusion of multiple users cannot obtain more data; even if the authorities compromise, biometric features of each user will not be leaked. Therefore, in terms of privacy-preserving biometric templates, fine-grained access, and dispute resolution, it is superior to the existing schemes.

Face Recognition

2011 ◽  
pp. 163-254
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Individual Identification ◽  
Modern Life ◽  
Identification Methods ◽  
Personal Security ◽  
Identification Method ◽  
The Individual ◽  
Biometric Features

In the modern life, the need for personal security and access control is becoming an important issue. Biometrics is the technology which is expected to replace traditional authentication methods that are easily stolen, forgotten and duplicated. Fingerprints, face, iris, and voiceprints are commonly used biometric features. Among these features, face provides a more direct, friendly and convenient identification method and is more acceptable compared with the individual identification methods of other biometrics features. Thus, face recognition is one of the most important parts in biometrics.

scholarly journals Facial soft biometric features for forensic face recognition

2015 ◽  
Vol 257 ◽  
pp. 271-284 ◽  
Author(s):  
Pedro Tome ◽  
Ruben Vera-Rodriguez ◽  
Julian Fierrez ◽  
Javier Ortega-Garcia
Keyword(s):  
Face Recognition ◽  
Biometric Features

scholarly journals Design of a Face Recognition System based on Convolutional Neural Network (CNN)

2020 ◽  
Vol 10 (3) ◽  
pp. 5608-5612 ◽  
Author(s):  
Y. Said ◽  
M. Barr ◽  
H. E. Ahmed
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition System ◽  
Great Variability ◽  
Artificial Vision ◽  
Surveillance Systems ◽  
Distributed Network ◽  
Biometric System ◽  
Face Recognition System ◽  
Deep Learning Model

Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.

scholarly journals Biometric, Fingervein, Machine L Implementation of a Security System, Using Captured Fingervein, Applying the Concepts of Machine Learning

2020 ◽  
Vol 9 (04) ◽  
pp. 24994-25007
Author(s):  
Oyinloye Oghenerukevwe Elohor ◽  
Akinbohun Folake ◽  
Thompson Aderonke ◽  
Korede Bashir
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Accurate Solution ◽  
Great Promise ◽  
Biometric System ◽  
Finger Vein ◽  
Similarities And Differences ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Biometric Features

This work explores the field of biometric finger vein recognition – which is the identification of individuals using the unique vein patterns under their finger skins. This work also includes the implementation of an Android fingerprint biometric system using the Android Near InfraRed (NIR) module, which exists to show the similarities and differences between the two (fingervein and fingerprint) prevalent biometric features. This work thus confirms that finger vein recognition shows great promise as an accurate solution to modern society’s problem of automated personal authentication

scholarly journals Biometric Approach for Confidentiality in Cloud Computing

2018 ◽  
Vol 10 (01) ◽  
pp. 65-70
Author(s):  
Pankaj Mishra ◽  
Dev Ratna Singh
Keyword(s):  
Private Information ◽  
Data Security ◽  
Personal Data ◽  
Security Measures ◽  
Biometric System ◽  
Security Systems ◽  
Biometric Security ◽  
Digital Database ◽  
Cyber Safety ◽  
Identity Cards

Nowadays, progress in technology have made life simple by giving us higher levels of knowledge through the innovation of various devices. However, all technical invention harbours the potential of invisible threats to its users. One leading danger is theft of private information and data. As digital database get more prevailing, user’s attempt to prevent their data with extremely encrypted Identity cards and passwords. However, the abuse and theft of these security measures are on the rise. Taking benefit of security fault in Identity cards result in the cards gets duplicated and get misused. This increasing conflict of the cyber safety has lead to the start of biometric security method. Defining the main variation between the methods of biometric system used to verify user identity will focus on the benefits and limitations of personal data security systems.

scholarly journals Face Detection and Gender Classification using Facial Features

2019 ◽  
Vol 8 (4) ◽  
pp. 6670-6674
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Facial Features ◽  
Gender Classification ◽  
Biometric System ◽  
Detection Techniques ◽  
The Face ◽  
And Gender ◽  
Two Stages

Face Recognition is the most important part to identifying people in biometric system. It is the most usable biometric system. This paper focuses on human face recognition by calculating the facial features present in the image and recognizing the person using features. In every face recognition system follows the preprocessing, face detection techniques. In this paper mainly focused on Face detection and gender classification. They are performed in two stages, the first stage is face detection using an enhanced viola jones algorithm and the next stage is gender classification. Input to the video or surveillance that video converted into frames. Select few best frames from the video for detecting the face, before the particular image preprocessed using PSNR. After preprocessing face detection performed, and gender classification comparative analysis done by using a neural network classifier and LBP based classifier

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