Design of iris recognition system based on ARM and DSP

2008 ◽  
Author(s):  
Zhou Tao ◽  
Xie Mei
Keyword(s):  
Iris Recognition ◽  
Recognition System

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

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.

scholarly journals Enhancing iris recognition system performance using templates fusion

2012 ◽  
Vol 3 (2) ◽  
pp. 133-140 ◽  
Author(s):  
Aly I. Desoky ◽  
Hesham A. Ali ◽  
Nahla B. Abdel-Hamid
Keyword(s):  
System Performance ◽  
Iris Recognition ◽  
Recognition System

Stand-off Iris Recognition System

2008 ◽  
Author(s):  
Frederick W. Wheeler ◽  
A. G. Amitha Perera ◽  
Gil Abramovich ◽  
Bing Yu ◽  
Peter H. Tu
Keyword(s):  
Iris Recognition ◽  
Recognition System
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