scholarly journals An effective segmentation method for iris recognition system

2008 ◽  
Author(s):  
R.Y.F. Ng ◽  
Yong Haur Tay ◽  
Kai Ming Mok
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Segmentation Method

scholarly journals Iris Segmentation

2018 ◽  
Vol 7 (2.5) ◽  
pp. 77
Author(s):  
Anis Farihan Mat Raffei ◽  
Rohayanti Hassan ◽  
Shahreen Kasim ◽  
Hishamudin Asmuni ◽  
Asraful Syifaa’ Ahmad ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Image Data ◽  
Recognition System ◽  
Environmental Condition ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Transform Method ◽  
Circular Hough Transform ◽  
Segmentation Methods

The quality of eye image data become degraded particularly when the image is taken in the non-cooperative acquisition environment such as under visible wavelength illumination. Consequently, this environmental condition may lead to noisy eye images, incorrect localization of limbic and pupillary boundaries and eventually degrade the performance of iris recognition system. Hence, this study has compared several segmentation methods to address the abovementioned issues. The results show that Circular Hough transform method is the best segmentation method with the best overall accuracy, error rate and decidability index that more tolerant to ‘noise’ such as reflection.  

Study on Iris Segmentation Method Using Chan-Vese Model

2013 ◽  
Vol 753-755 ◽  
pp. 2995-2999
Author(s):  
Ying Chen ◽  
Feng Yu Yang
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Iris Localization ◽  
Zero Crossing ◽  
Critical Step ◽  
Iris Image ◽  
Pros And Cons

The first and critical step in the process of an iris recognition system is iris segmentation. Firstly, we detailedly describe the process of pupil and iris localization based on Chan-Vese model. Secondly, we describe the process of unwrapping iris annule region, and obtain rectangular image with the same width but different height. Thirdly, cut rectangular iris image to get normalized image. Fourthly, Multi-channel 2D Gabor, 1-D wavelets and zero-crossing methods were used to extract feature; consequently, decidability indexes of intra-class and inter-class were obtained. Finally, comparatively analyze the pros and cons of the proposed method. Three public iris images databases were taken as experimental samples, the experimental results on these image samples demonstrate that the proposed algorithm has certain advantage.

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.

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