Iris Recognition System Using Statistical Features for Biometric Identification

Kyaw, Khin

doi:10.1109/icect.2009.129

Cited by 13 publications

(2 citation statements)

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“…Richard et al [18] discussed the effect of noises such as eyelids, eyelashes, reflection and pupil noises on iris segmentation and proposed an approach to give a solution for compensating all four types of noises to achieve higher accuracy rate, but the dataset used here not enough to test this approach. [19] applied Sobel edge detector. By using this detector, the gradient value can be seen easily.…”

Section: Related Workmentioning

confidence: 99%

An efficient iris segmentation approach

2011

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Iris recognition system became a reliable system for authentication and verification tasks. It consists of five stages: image acquisition, iris segmentation, iris normalization, feature encoding, and feature matching. Iris segmentation stage is one of the most important stages. It plays an essential role to locate the iris efficiently and accurately. In this paper, we present a new approach for iris segmentation using image processing technique. This approach is composed of four main parts. (1) Eliminating reflections of light on the eye image based on inverting the color of the grayscale image, filling holes in the intensity image, and inverting the color of the intensity image to get the original grayscale image without any reflections.(2) Pupil boundary detection based on dividing an eye image to nine sub-images and finding the minimum value of the mean intensity for each sub-image to get a suitable threshold value of pupil. (3) Enhancing the contrast of outer iris boundary using exponential operator to have sharp variation. (4) Outer iris boundary localization based on applying a gray threshold and morphological operations on the rectangular part of an eye image including the pupil and the outer boundaries of iris to find the small radius of outer iris boundary from the center of pupil. The proposed approach has been tested on CASIA v1.0 iris image database and other collected iris image database. The experimental results show that the approach is able to detect pupil and outer iris boundary with high accuracy results approximately 100% and reduce time consuming.Keywords-iris segmentation; pupil detection; minimum and mean intensity of pupil; iris recognition system; image processing tools.

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Section: Related Workmentioning

confidence: 99%

An efficient iris segmentation approach

2011

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“…Various algorithms have been adopted by researchers for feature extraction which are based on different transform. A little research has been recorded using statistical techniques for iris recognition [6].…”

Section: Introductionmentioning

confidence: 99%

Iris Feature Extraction and Recognition based on Gray Level Co-occurrence Matrix (GLCM) Technique

Rasool¹

2018

IJCA

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Biometric features have received great attention for many applications. Iris recognition is one of the most modern biometric technique that is used for accurate and reliable authentication. Recently, Gray-Level Cooccurrence Matrix (GLCM) is one of the advanced techniques used for features extraction. In this paper, an iris recognition system proposed involves; preprocessing, feature extraction, and matching processes. After the preprocessing process, the feature extraction technique based on GLCM has been applied to pure iris region to extract features. Only one of the second-order statistical features known as contrast will be calculated from the generated co-occurrence matrix and stored it as a numerical feature vector in CASIA-v4.0-iris database. During recognition, the matching metric based on Euclidean distance has been used for authentication. Results have demonstrated (99.5%) highly accuracy rate with (0.02) FAR, and (0.01) FRR.

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