Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

2016 ◽  
Vol 6 (4) ◽  
pp. 1637
Author(s):  
Vineet Kumar ◽  
Abhijit Asati ◽  
Anu Gupta
Keyword(s):  
Hough Transform ◽  
Near Infrared ◽  
Boundary Detection ◽  
Morphological Operations ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Circular Hough Transform ◽  
Map Generation ◽  
Sobel Edge Detection ◽  
Binary Edge

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results.

scholarly journals Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

2016 ◽  
Vol 6 (4) ◽  
pp. 1637 ◽  
Author(s):  
Vineet Kumar ◽  
Abhijit Asati ◽  
Anu Gupta
Keyword(s):  
Hough Transform ◽  
Near Infrared ◽  
Boundary Detection ◽  
Morphological Operations ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Circular Hough Transform ◽  
Map Generation ◽  
Sobel Edge Detection ◽  
Binary Edge

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results.

scholarly journals Localization and segmentation of optic disc in retinal images using Circular Hough transform and Grow Cut algorithm

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2003 ◽  
Author(s):  
Muhammad Abdullah ◽  
Muhammad Moazam Fraz ◽  
Sarah A. Barman
Keyword(s):  
Optic Disc ◽  
Hough Transform ◽  
Retinal Image ◽  
Diagnostic System ◽  
Retinal Images ◽  
Computer Assisted ◽  
Morphological Operations ◽  
Circular Hough Transform ◽  
Disc Center ◽  
Unique Method

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the Circular Hough transform and the Grow Cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the Circular Hough transform, and the Grow Cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.

Fast and Effective Algorithm of Iris Localization Based on Hough Transform

2014 ◽  
Vol 519-520 ◽  
pp. 788-793
Author(s):  
Bao Qiang Wang ◽  
Lin Zhang
Keyword(s):  
Hough Transform ◽  
Polar Coordinates ◽  
Effective Algorithm ◽  
Horizontal Edge ◽  
Edge Image ◽  
Iris Localization ◽  
Boundary Parameter ◽  
Relationship Of ◽  
Binary Edge

The purpose of the paper is to research a fast and effective algorithm of iris localization based on Hough transform, for improving the quality of iris localization. The methods of practice include as follows. Firstly, the pupil center is estimated by using of a metric. Secondly, the binary edge image is transformed into the polar coordinates. After the rules of horizontal edge selection are used to select horizontal edge, the edge-selected image is transformed into the Cartesian coordinates. Finally, the Hough transform and the coupling relationship of the boundaries are used to determine the parameters of boundaries, and the biggest boundary parameter is selected for the estimation of the iris boundary parameter. Experiments indicate that the proposed algorithm is effective and available.

scholarly journals IRIS Recognition using Hough Transform

2019 ◽  
Vol 8 (9S3) ◽  
pp. 504-507
Keyword(s):  
Light Source ◽  
Hough Transform ◽  
Iris Recognition ◽  
Near Infrared ◽  
Hill Climbing ◽  
Iris Localization ◽  
Visible Wavelength ◽  
Area Of Interest ◽  
Iris Identification ◽  
Close Distance

In most iris identification systems, the complete image acquires constraints are understood. These Constrain include near-infrared (NIR) illumination to release the iris texture and close distance from the capturing device. In recent advances to different illumination technologies introduced in images captured in the environment. This environment includes a visible wavelength (VW) light source at-a-distance over the close distance from the capturing device. For accurate Iris identification at-a-distance, eye images require improvement of effective strategies, while setting the light source at a distance from the planar view of the iris. Effectively performing feature extraction technique for Near-Infrared and Visible wavelength images, that were collected in an uncontrolled stage. The identification of iris accuracy on the publicly available databases was then measured. This paper presents a preprocessing of Iris Recognition using Hough Transform (HT) for Iris Area of interest (AOI) and rubber-sheeting the model captured using linear stretching and rotation for normalization. The HT is used to filter and contrast stretch the iris regions from multispectral iris images. A basic purpose of this research is to envelop a design and implement IRIS-recognition at a distance (IAAD) by adopting a frequency and wavelength-based Hough transform for accurate feature selection [1][2]. The proposed method is described as follows: Initially, the input iris image will be subjected to pre-processing while extracting features with differences from local extrema and maxima conditions, using a regular shape filling Hough transform [3][4]. The iris localization and detection consists of a hill climbing segmentation approach that is based on geometric shape Hough measure. Proposed in comparison to the contemporary

scholarly journals Circular Hough Transform for Iris localization

2012 ◽  
Vol 2 (5) ◽  
pp. 114-121 ◽  
Author(s):  
Noureddine Cherabit ◽  
Fatma Zohra Chelali ◽  
Amar Djeradi
Keyword(s):  
Hough Transform ◽  
Iris Localization ◽  
Circular Hough Transform
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