An Efficient Iris Segmentation Method for Non-Ideal Iris Images

2013 ◽  
Vol 658 ◽  
pp. 597-601
Author(s):  
Xiao Wen Xu
Keyword(s):  
Level Set ◽  
Iris Recognition ◽  
Outer Boundary ◽  
Least Square ◽  
Projection Algorithm ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Fitting Algorithm ◽  
Segmentation Accuracy ◽  
The Impact

Segmenting the non-ideal iris images accurately is a main problem for iris recognition, due to the impact of the eyelids, eyelashes and deformation. The paper presents an iris segmentation method based on an improved level set. Firstly, we used gray projection algorithm to locate the pupil. Secondly, we applied the least square fitting algorithm to estimate the boundary between the pupil and the iris. Finally, we used the level set method to accurately segment the iris. Experimental results demonstrate the segmentation accuracy for outer boundary of the iris is 98.59%. The method presented in this paper is superior to Daugman method and Hough transform algorithm in iris segmentation, especially for non-ideal iris images.

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.  

scholarly journals Noncircular Iris Segmentation Based on Weighted Adaptive Hough Transform Using Smartphone Database

2018 ◽  
Vol 15 (2) ◽  
pp. 739-743 ◽  
Author(s):  
Noor Amjed ◽  
Fatimah Khalid ◽  
Rahmita Wirza O. K. Rahmat ◽  
Hizmawati Binit Madzin
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Recognition System ◽  
Iris Segmentation ◽  
Negative Image ◽  
Processing Step ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
High Level ◽  
Imaging Conditions

Iris segmentation methods work based on ideal imaging conditions which produce good output results. However, the segmentation accuracy of an iris recognition system significantly influences its performance, especially with data that captured in unconstrained environment of the Smartphone. This paper proposes a novel segmentation method for unconstrained environment of the Smartphone videos based on choose the best frames from the videos and try to enhance the contrast of this frames by applying the two fuzzy logic membership functions on the negative image which delimit between dark and bright regions in able to make the dark region darker and the bright region brighter. This pre-processing step Facilitates the work of the Weighted Adaptive Hough Transform to automatically find the diameter of the iris region to apply the osiris v4.1. The proposed method results on the video of (Mobile Iris Challenge Evaluation (MICHE))-I, iris databases indicate a high level of accuracy and more efficient computationally using the proposed technique.

Detecting Eyelash and Reflection for Accurate Iris Segmentation

2003 ◽  
Vol 17 (06) ◽  
pp. 1025-1034 ◽  
Author(s):  
Wai-Kin Kong ◽  
David Zhang
Keyword(s):  
Statistical Model ◽  
Iris Recognition ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Edge Detector ◽  
Recognition Error ◽  
Weak Reflection

Accurate iris segmentation is presented in this paper, which is composed of two parts, reflection detection and eyelash detection. Eyelashes are classified into two categories, separable and multiple. An edge detector is applied to detect separable eyelashes, and intensity variances are used to recognize multiple eyelashes. Reflection is also divided into two types, strong and weak. A threshold and statistical model is proposed to recognize the strong and weak reflection, respectively. We have developed an iris recognition approach for testing the effectiveness of the proposed segmentation method. The results show that the proposed method can reduce recognition error for the iris recognition approach.

scholarly journals Efficient Iris Localization via Optimization Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qi Wang ◽  
Zhipeng Liu ◽  
Shu Tong ◽  
Yuqi Yang ◽  
Xiangde Zhang
Keyword(s):  
Optimization Model ◽  
Iris Recognition ◽  
Outer Boundary ◽  
Descent Method ◽  
Least Square ◽  
Experimental Result ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Supervised Descent Method

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

Iris Segmentation for Non-ideal Images

2015 ◽  
Vol 74 (3) ◽  
Author(s):  
Nasharuddin Zainal ◽  
Abduljalil Radman ◽  
Mahamod Ismail ◽  
Md Jan Nordin
Keyword(s):  
Iris Recognition ◽  
Recognition Algorithm ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
West Virginia University ◽  
Segmentation Methods ◽  
Biometric Systems ◽  
Segmentation Algorithms ◽  
Recognition Systems ◽  
Constrained Conditions

Iris recognition has been regarded as one of the most reliable biometric systems over the past years. Previous studies have shown that the performance of iris recognition systems highly dependent on the performance of their segmentation algorithms. Iris segmentation is the process to isolate the iris region from the surrounded structures of the eye image. However, several iris segmentation algorithms have been developed in the literature, but their segmentation and recognition accuracies drastically degrade with non-ideal iris images acquired in less constrained conditions. Thus, it is crucial to develop a new iris segmentation method to improve iris recognition using non-ideal images. Hence, the objective of this paper is an iris segmentation method on the basis of optimization to isolate the iris region from non-ideal iris images such those affected by reflections, blurred boundaries, eyelids occlusion, and gaze-deviation. Experimental results on the off axis/angle West Virginia University (WVU) iris database demonstrated the superiority of the developed method over state-of-the-art iris segmentation methods considered in this paper. The performance of an iris recognition algorithm based on the developed iris segmentation method was observed to be improved.  

EM-Based Nonideal Iris Boundary Localization

2012 ◽  
Vol 562-564 ◽  
pp. 2073-2078
Author(s):  
Hong Lin Wan ◽  
Bao Sheng Li ◽  
Min Han ◽  
Deng Wang Li
Keyword(s):  
Em Algorithm ◽  
Iris Recognition ◽  
Outer Boundary ◽  
Iris Segmentation ◽  
Critical Step ◽  
Novel Method ◽  
Equilibrium Scheme ◽  
Iris Center

Nonideal iris segmentation is a great challenge in iris recognition, and many researchers have stressed this problem. Since critical step of segmentation is localizing iris center and detecting interior/outer boundaries, we presents a novel method based on EM algorithm to deal with it. EM algorithm is capable of automatic threshold, therefore candidate pupil can be obtained and followed by an innovated fast iris center searching by using strings equilibrium scheme. We also give the region-based outer boundary localization with implementation of order statistical filters (OSF). Experiments demonstrate a high correct segmentation ratio (CSR) of more than 98% has been achieved when using CASIA-IrisV3 Interval and CASIA-IrisV3 Lamp databases.

Noisy Iris Segmentation with Reflections Removal Using Probable Boundary Edge Detector

2012 ◽  
Vol 236-237 ◽  
pp. 1116-1121 ◽  
Author(s):  
Min Wang ◽  
Ning Wang ◽  
Xiao Gui Yao
Keyword(s):  
Iris Recognition ◽  
State Of The Art ◽  
Image Database ◽  
Recognition System ◽  
Experimental Results ◽  
Boundary Edge ◽  
Iris Segmentation ◽  
Edge Detector ◽  
Segmentation Methods ◽  
Segmentation Accuracy

Iris segmentation plays an important role in iris recognition system. Most of segmentation methods are affected by reflection spots, eyelash and eyelid etc. The goal of this work is to accurately segment the iris using Probable boundary (Pb) edge detector after horizontal-vertical weighted reflections removal. Experimental results on the challenging iris image database CASIA-Iris-Thousand with reflection spots sample demonstrate that the iris segmentation accuracy of the proposed methods outperforms state-of-the-art methods.

scholarly journals A fast specular reflection removal based on pixels properties method

2020 ◽  
Vol 9 (6) ◽  
pp. 2358-2363
Author(s):  
Shahrizan Jamaludin ◽  
Nasharuddin Zainal ◽  
W. Mimi Diyana W. Zaki
Keyword(s):  
Execution Time ◽  
Iris Recognition ◽  
Specular Reflection ◽  
Recognition System ◽  
The Other ◽  
Iris Segmentation ◽  
Removal Method ◽  
Human Iris ◽  
Segmentation Accuracy ◽  
Recognition Systems

Iris recognition has been around for many years due to an extensive research on the uniqueness of human iris. It is well known that the iris is not similar to each other which means every human in the planet has their own iris pattern and cannot be shared. One of the main issues in iris recognition is iris segmentation. One element that can reduce the accuracy of iris segmentation is the presence of specular reflection. Another issue is the speed of specular reflection removal since the iris recognition system needs to process a lot of irises. In this paper, a specular reflection removal method was proposed to achieve a fast and accurate specular reflection removal. Some modifications were implemented on the existing pixels properties method. Based on the results, the proposed method achieved the fastest execution time, the highest segmentation accuracy and the highest SSIM compared to the other methods. This proves that the proposed method is fast and accurate to be implemented in the iris recognition systems.

scholarly journals Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1434
Author(s):  
Yung-Hui Li ◽  
Wenny Ramadha Putri ◽  
Muhammad Saqlain Aslam ◽  
Ching-Chun Chang
Keyword(s):  
Iris Recognition ◽  
Outer Boundary ◽  
Semantic Segmentation ◽  
Recognition System ◽  
Segmentation Algorithm ◽  
Iris Segmentation ◽  
Specular Reflections ◽  
Boundary Estimation ◽  
Segmentation Methods ◽  
The Mean

Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.

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