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.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

Survey on Segmentation to Iris Recognition System

2012 ◽  
Vol 3 (4) ◽  
pp. 514-515
Author(s):  
Meenakshi BK Meenakshi BK ◽  
◽  
Prasad M R Prasad M R
Keyword(s):  
Iris Recognition ◽  
Recognition System

A Survey on Iris Recognition System

2019 ◽  
Vol 7 (7) ◽  
pp. 302-307
Author(s):  
Prabhat Kumar ◽  
Manish Ahirwar ◽  
Anjna Deen
Keyword(s):  
Iris Recognition ◽  
Recognition System

Cancelable Iris Recognition System with Pre-trained Convolutional Neural Networks

2019 ◽  
Vol 28 (1) ◽  
pp. 95-101
Author(s):  
Eman M. Omran ◽  
Randa F. Soliman ◽  
Ayman A. Eisa ◽  
Nabil A. Ismail ◽  
Fathi E. Abd El-Samie
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Iris Recognition ◽  
Recognition System

Determination of Sample Size and Influencing Factors to Characterize Faulting in Jointed Concrete Pavements

2021 ◽  
pp. 036119812199670
Author(s):  
Ruohan Li ◽  
Jorge A. Prozzi
Keyword(s):  
Sample Size ◽  
Plain Concrete ◽  
Concrete Pavements ◽  
Portland Cement Concrete ◽  
Federal Highway ◽  
Significant Difference ◽  
Jointed Concrete Pavement ◽  
Functional Classes ◽  
High Precipitation ◽  
Departments Of Transportation

The objective of this study is to evaluate the field variability of jointed concrete pavement (JCP) faulting and its effects on pavement performance. The standard deviation of faulting along both the longitudinal and transverse directions are calculated. Based on these, the overall variability is determined, and the required sample sizes needed for a given precision at a certain confidence level are calculated and presented. This calculation is very important as state departments of transportation are required to report faulting every 0.1 mi to the Federal Highway Administration as required by the 2015 FAST Act. On average, twice the number of measurements are needed on jointed reinforced concrete pavements (JRCP) to achieve the same confidence and precision as on jointed plain concrete pavements (JPCP). For example, a sample size of 13 is needed to achieve a 95% confidence interval with a precision of 1.0 mm for average faulting of JPCP, while 26 measurements are required for JRCP ones. Average faulting was found to correlate with several climatic, structural, and traffic variables, while no significant difference was found between edge and outer wheelpath measurements. The application of Portland cement concrete overlay and the use of dowel bars (rather than aggregate interlock) are found to significantly reduce faulting. Older sections located on higher functional classes, and in regions of high precipitation or where the daily temperature change is larger, tend to have higher faulting, and might require larger samples sizes as compared with the rest when faulting surveys are to be conducted.

Age dependency of the diabetes effects on the iris recognition systems performance evaluation results

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammadreza Azimi ◽  
Seyed Ahmad Rasoulinejad ◽  
Andrzej Pacut
Keyword(s):  
Iris Recognition ◽  
Recognition Task ◽  
Aging Effect ◽  
Recognition System ◽  
Health Condition ◽  
Age Dependency ◽  
Biometric Systems ◽  
Kolmogorov Smirnov ◽  
Recognition Systems ◽  
Smirnov Test

AbstractIn this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals’ age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches’s t-test and Kolmogorov–Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.

Sign in / Sign up

Export Citation Format

Share Document