scholarly journals Multimodal Biometric Authentication Using Fingerprint and Iris Recognition in Identity Management

2013 ◽  
Author(s):  
Kamer Vishi ◽  
Sule Yildirim Yayilgan
Keyword(s):  
Iris Recognition ◽  
Identity Management ◽  
Biometric Authentication

USABLE SECURITY IN BIOMETRIC AUTHENTICATION SYSTEMS

2021 ◽  
Vol 17 (1) ◽  
pp. 287-292
Author(s):  
Adriana-Meda UDROIU ◽  
Ștefan-Antonio DAN-ȘUTEU
Keyword(s):  
Iris Recognition ◽  
Accurate Method ◽  
Biometric Authentication ◽  
Usable Security ◽  
Security Systems ◽  
Field Methods ◽  
Secure Systems ◽  
Authentication System ◽  
Secure Authentication ◽  
Pattern Variability

Abstract: We introduce the term usable security to refer to security systems, models, mechanisms and applications that have as the main goal usability. Secure systems cannot exist without secure authentication methods. Thus we outline biometric authentication methods and we focus on iris recognition because is the most reliable and accurate method for human identification]. The most important advantage of iris biometric over other biometrics is that irises have enormous pattern variability meaning that the variation between individual is almost maximum and variation for any person across time or conditions is minimum. Taking into consideration this observations, this survey covers researches in this field, methods of technical implementation and the usability of this method as an authentication system on iOS environment.

scholarly journals Detection of Involuntary Iris Scanning for Enhanced Biometric Security

2019 ◽  
Vol 8 (9S) ◽  
pp. 526-532
Keyword(s):  
Sample Size ◽  
Iris Recognition ◽  
Recognition System ◽  
Biometric Authentication ◽  
Eye Blink ◽  
Biometric Security ◽  
Significant Difference ◽  
Proactive Measures ◽  
Average Size ◽  
Iris Scanning

Although iris recognition system is considered as most robust, hard to counterfeit and the most secure system of biometric authentication. However the existing system fails to detect a forced authentication which might be misused by criminals to unlock the user's account. In this paper we examine the conditions in which a real user is forcibly presented in front of iris scanner on gun point to unlock the account. In this case a significant difference is noted in the area of iris visibility with respect to user's normal iris area visibility. An abnormal eye blink is also detected in forced condition. We successfully design and developed an algorithm to detect such conditions to protect the users from criminals when a user is forcibly presented to an iris scanner to unlock their account. A sample size of 65 volunteers are taken to record the iris authentication in both the conditions i.e. normal with consent of user and forced under without user’s consent. The average size of iris is recorded 10.1 mm while it expands on 13.2 mm (average) in fear when iris is being scanned forcibly by criminals. We conclude that a variation of 2 to 3 mm in iris exposure is a clear biomarker to indicate some presence of criminal traces and take proactive measures to prevent losses.

scholarly journals An optimized rubber sheet model for normalization phase of IRIS recognition

2020 ◽  
Vol 1 (3) ◽  
pp. 126-134
Author(s):  
Selvamuthukumaran S. ◽  
Ramkumar T. ◽  
Shantharajah Shantharajah
Keyword(s):  
Iris Recognition ◽  
Histogram Equalization ◽  
Normalization Method ◽  
Polar Coordinates ◽  
Experimental Result ◽  
Biometric Authentication ◽  
Spherical Coordinates ◽  
Rubber Sheet ◽  
Segmented Image ◽  
Human Iris

Iris recognition is a promising biometric authentication approach and it is a very active topic in both research and realistic applications because the pattern of the human iris differs from person to person, even between twins. In this paper, an optimized iris normalization method for the conversion of segmented image into normalized form has been proposed. The existing methods are converting the Cartesian coordinates of the segmented image into polar coordinates. To get more accuracy, the proposed method is using an optimized rubber sheet model which converts the polar coordinates into spherical coordinates followed by localized histogram equalization. The experimental result shows the proposed method scores an encouraging performance with respect to accuracy.

scholarly journals Extending identity management system with multimodal biometric authentication

2016 ◽  
Vol 13 (2) ◽  
pp. 313-334
Author(s):  
Bojan Jovanovic ◽  
Ivan Milenkovic ◽  
Marija Bogicevic-Sretenovic ◽  
Dejan Simic
Keyword(s):  
Identity Management ◽  
Data Access ◽  
Management Systems ◽  
Biometric Authentication ◽  
Multimodal Biometrics ◽  
Biometric Data ◽  
Multifactor Authentication ◽  
Biometric Systems ◽  
Identity Management Systems ◽  
Identity Management System

Techniques for authentication that are used in today's identity management systems are vulnerable when they are used over the network. In order to prevent fraud and unauthorized data access, it is important to ensure the identity of the person who submitted authentication credentials. The authentication process can be additionally secured by using biometric data for user verification. Moreover, precision of biometric authentication can be improved by the use of multimodal biometrics. This paper presents a system which has been designed for identity management based on FreeIPA solution for digital identity management and MMBio framework for multimodal biometrics. Proposed system provides multifactor authentication, where MMBio framework is used for handling user biometric data. Developed prototype confirms possible integration of identity management and multimodal biometric systems.

scholarly journals GA based Feature Selection for Multimodal Biometric Authentication

2021 ◽  
Vol 12 (2) ◽  
pp. 526-538
Author(s):  
A. Saravana Priya ◽  
◽  
Dr. Rajeswari Mukesh
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Identity Management ◽  
Vital Role ◽  
Optimization Techniques ◽  
Physical Contact ◽  
Biometric Authentication ◽  
Feature Extraction Method ◽  
Wide Range ◽  
Artificial Neural Network Ann

Multi-modal biometric authentication effectively replaces uni-modal biometric authentication system towards addressing a wide range of technical glitches in identity management and authentication. Legitimacy is playing a vital role in banking, military, and healthcare sectors where highly secure, strategic and confidential data transmission is involved. By integrating many independent biometric systems, one can overcome the problems of spoofing. However, there is lack of a simple, efficient and sufficient biometric authentication. Hence, the present study focuses on designing and implementing a multi-modal biometric authentication using a Genetic Algorithm (GA) based feature extraction method. The proposed research focuses on extracting human Skeleton and Human face feature using 3D Imaging technology. This modelling technique is used to capture human joints including the depth data to improve the efficiency of the system. The proposed research is subdivided into three phases. These are, image preprocessing (MinMax method), feature extraction using Heuristic Optimization Techniques (HOT), and Personnel recognition via the Artificial Neural Network (ANN). The Performance of the proposed method is evaluated based on the measure of FAR, FRR and accuracy. Finally, the performance of proposed approach is compared with existing techniques like GA, Neural network, etc. Combined Biometric is done in an unobtrusive way whereas other human recognition needs physical contact.

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