A Literature Review on Iris Segmentation Techniques for Iris Recognition Systems

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.

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A fast specular reflection removal based on pixels properties method

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i6.2524 ◽

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.

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A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

Journal of Electrical and Computer Engineering ◽

10.1155/2016/4709876 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Vineet Kumar ◽

Abhijit Asati ◽

Anu Gupta

Keyword(s):

Iris Recognition ◽

Near Infrared ◽

Outer Boundary ◽

Iris Segmentation ◽

Low Contrast ◽

Iris Image ◽

Average Accuracy ◽

Version 2.0 ◽

Recognition Systems ◽

Pupil Localization

Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. The iris segmentation is a process of detecting the pupil, iris’s outer boundary, and eyelids in the iris image. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR) illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and occlusions by the eyelids, eyelashes, and eyebrow hair. In the proposed method, first, a novel edge-map is created from the iris image, which is based on combining the conventional thresholding and edge detection based segmentation techniques, and then, the general circular Hough transform (CHT) is used to find the pupil circle parameters in the edge-map. Our main contribution in this research is a novel edge-map creation technique, which reduces the false edges drastically in the edge-map of the iris image and makes the pupil localization in the noisy NIR images more accurate, fast, robust, and simple. The proposed method was tested with three iris databases: CASIA-Iris-Thousand (version 4.0), CASIA-Iris-Lamp (version 3.0), and MMU (version 2.0). The average accuracy of the proposed method is 99.72% and average time cost per image is 0.727 sec.

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Semantic Segmentation of Iris using U-Net in Deep Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2614.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 2024-2028

Keyword(s):

Deep Learning ◽

Iris Recognition ◽

Semantic Segmentation ◽

Iris Segmentation ◽

The Past ◽

Security Control ◽

Recognition Systems

In the field of medicine, iris segmentation has become a great field of interest from the past few years. Iris segmentation is also largely used in iris recognition systems [3] which are extensively used in security control [1][2]. Here iris segmentation is done using semantic segmentation which is based on the U-Net architecture. The typical U-net architecture contains two pathscontracting path containing convolutional and pooling layers and the expanding path consists of transposed convolutional operations. The UBIRIS dataset is trained on the traditional UNet model with some modifications according to the size of the images present in the UBIRIS dataset. The results obtained were very close to the ground truths and accuracy obtained is also appreciable.

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

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0246 ◽

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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