Boundary Detection for Non-Ideal Iris Based on Gray Transform

2013 ◽  
Vol 441 ◽  
pp. 682-686
Author(s):  
Hong Lin Wan ◽  
Bao Sheng Li ◽  
Hong Sheng Li
Keyword(s):  
Iris Recognition ◽  
Outer Boundary ◽  
Boundary Detection ◽  
Recognition System ◽  
Experimental Results ◽  
Low Contrast ◽  
Localization Accuracy ◽  
Localization Method ◽  
Key Issues

Boundary localization is one of the key issues for reliable iris recognition system. For non-ideal iris images, eyelashes or eyelids occlusions and low contrast between iris and sclera will lead to inaccurate boundary localization. Specifically, if the intensive transition from iris to sclera is too smooth, outer boundary localization will be very difficult. To stress the problem, in this paper the boundary localization method is proposed in which nonlinear gray transformation is innovated in outer boundary localization process. The experimental results depict that our algorithm have improved the localization accuracy for non-ideal iris compared to the classical algorithms.

A Robust Boundary Localization for Degraded Iris Images

2013 ◽  
Vol 846-847 ◽  
pp. 986-990
Author(s):  
Tian Ping Li ◽  
Xiao Wei Wang
Keyword(s):  
Iris Recognition ◽  
Outer Boundary ◽  
Recognition System ◽  
Experimental Results ◽  
Gray Level ◽  
Low Contrast ◽  
Localization Accuracy ◽  
Localization Method ◽  
Key Issues

Boundary localization is one of the key issues for reliable iris recognition system. For degraded iris images, some frequently-occurred cases such as dominant texture patterns, eyelashes or eyelids occlusions, low contrast between iris and sclera, and pupil deviation, will lead to inaccurate boundary localization. Specifically, if the intensive transition from iris to sclera is too smooth, outer boundary localization will be very difficult. To stress the problem, in this paper the boundary localization method is proposed in which nonlinear gray-level transformation is innovated in outer boundary localization process. The experimental results depict that our algorithm have improved the localization accuracy for degraded iris compared to the classical algorithms.

scholarly journals A Novel Iris Segmentation Scheme

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Chen-Chung Liu ◽  
Pei-Chung Chung ◽  
Chia-Ming Lyu ◽  
Jui Liu ◽  
Shyr-Shen Yu
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Image Database ◽  
Recognition System ◽  
Experimental Results ◽  
Circle Fitting ◽  
Iris Segmentation ◽  
Iris Image ◽  
Conventional Methods ◽  
Key Steps

One of the key steps in the iris recognition system is the accurate iris segmentation from its surrounding noises including pupil, sclera, eyelashes, and eyebrows of a captured eye-image. This paper presents a novel iris segmentation scheme which utilizes the orientation matching transform to outline the outer and inner iris boundaries initially. It then employs Delogne-Kåsa circle fitting (instead of the traditional Hough transform) to further eliminate the outlier points to extract a more precise iris area from an eye-image. In the extracted iris region, the proposed scheme further utilizes the differences in the intensity and positional characteristics of the iris, eyelid, and eyelashes to detect and delete these noises. The scheme is then applied on iris image database, UBIRIS.v1. The experimental results show that the presented scheme provides a more effective and efficient iris segmentation than other conventional methods.

scholarly journals Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Ying Chen ◽  
Yuanning Liu ◽  
Xiaodong Zhu ◽  
Huiling Chen ◽  
Fei He ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Quality Evaluation ◽  
Feature Fusion ◽  
Recognition System ◽  
Experimental Results ◽  
Iris Image ◽  
Local Quality ◽  
Evaluation Algorithm ◽  
Evaluation Parameters

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks’ information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples’ own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

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 An improved iris recognition system based on the fusion of the curvelet and DTCWT

2020 ◽  
pp. 8-12
Author(s):  
Nguyen Nam Phuc, Le Tien Hung, Nguyen Quoc Trung, Ha Huu Huy Nguyen
Keyword(s):  
Wavelet Transform ◽  
Random Forest ◽  
Iris Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Random Forest Classifier ◽  
Experimental Results ◽  
Complex Wavelet Transform ◽  
Decision Level ◽  
Complex Wavelet

In recent years, iris recognition has been emerged as one of the most popular biometric techniques because it guarantees high universality, distinctiveness, permanence, collectability, performance, acceptability, circumvention. In the paper we propose an improved system for iris recognition with high accuracy by fusing curvelet and dual tree complex wavelet transform. In our system, the main features are extracted from pre-processed/normalized iris images using both curvelet and Dual Tree Complex Wavelet Transform (DTCWT) tranforms. After performing different classifiers independently, all the results are fused to get final classification in the decision level to increase the accuracy of system. Finally, the random forest classifier and CATIA dataset are used to measure the performance of the proposed method. The experimental results show that the technique of the paper based on fusion of the curvelet and DTCWT is promising when compared with other existing similar techniques.

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.

Iris Boundary Localization Based on Improve Hough Transform

2013 ◽  
Vol 760-762 ◽  
pp. 1576-1580
Author(s):  
Guo Yu Zhang ◽  
Hui Zhao ◽  
Min Han ◽  
Li Ling Chen
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Traditional Method ◽  
Iris Recognition ◽  
Recognition System ◽  
Low Contrast ◽  
Exterior Boundary ◽  
Iris Image ◽  
Interior Boundary ◽  
Key Steps

ris location is one of the key steps of iris recognition system. Non-ideal iris image has some problems, such as eyelid and eyelash occlusion, low contrast of iris and sclera, uneven illumination, and so on. Because of that, its difficult to identify the boundary, especially the exterior boundary. Therefore, this paper proposes a method based on the improved Hough Transform. First, use the minimum method to find the datum point in the pupil, after that identify the valid area of the interior boundary base on that point. Apply the improved Hough Transform to that valid area to identify the interior boundary of the iris image. Then regard the center of the interior circle as our new datum point, use the same method to identify the exterior boundary. Experiment results show that our algorithm has higher accuracy than traditional method on the non-ideal iris image segmentation.

An Improved Low Contrast Image in Normalization Process for Iris Recognition System

2018 ◽  
pp. 495-505
Author(s):  
Abdulrahman Aminu Ghali ◽  
Sapiee Jamel ◽  
Kamaruddin Malik Mohamad ◽  
Shamsul Kamal Ahmad Khalid ◽  
Zahraddeen Abubakar Pindar ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Low Contrast ◽  
Contrast Image ◽  
Normalization Process

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.

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