Robust iris location in close-up images of the eye

Trucco, Emanuele; Razeto, Marco

doi:10.1007/s10044-005-0004-8

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…By comparing the ultimate measurement accuracy and average multi-direction offsets, the segmentation accuracy of algorithm proved that the improved watershed and active contour segmentation algorithm was efficient, accurate and reliable. From Trucco, 15 we can know that processing one image (350 * 270 pixel) with unoptimized and uncompiled Matlab code is about 5 seconds. The method he proposed may consume about 100 ms an image.…”

Section: Discussionmentioning

confidence: 99%

“…A number of researchers, such as prefer to use methods based on Hough-transform to segment the iris edge from image, they are Ma, 8 Huang, 9 Lim, 10 and Yuan 11 respectively. And there are also other methods were proposed, such as scanning method, 12 Least squares fitting algorithm, 13 canny edge detection algorithm 14 and active ellipses, 15 16 and so on.…”

Section: Introductionmentioning

confidence: 99%

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Iris Edge Extraction and Location by Advanced Watershed and Active Contour Method

Meng¹,

Ren²,

Pan³

et al. 2015

j comput theor nanosci

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The final performance of iris recognition system is partly dependent on the accuracy of iris segmentation (iris localization or iris edge detection). A new iris location method based on watershed transform and an active contour model was proposed in this paper. To avoid over-segmentation problem, watershed transform with a marking function and minima imposition was proposed. The active contour model converged to the precise edge from the initial contour obtained by the watershed transform. The method we used can improve the speed of the image segmentation. It was applied on 200 iris images with 90.04% of the test targets successfully segmented. And the processing time of an image is less than the others. Algorithm proposed in this paper proved the combination method is an effective method for segmentation of iris boundary.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iris Edge Extraction and Location by Advanced Watershed and Active Contour Method

Meng¹,

Ren²,

Pan³

et al. 2015

j comput theor nanosci

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show abstract

“…Another approach based on elliptical shapes is described in [76]. The proposed method uses an active ellipse modeling technique for estimating the outer iris boundary.…”

Section: Methods Based On A-priori Modelsmentioning

confidence: 99%

Cross Disciplinary Biometric Systems

Liu¹,

Mago²

2012

Intelligent Systems Reference Library

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“…Other regions successfully detected as containing reflections and in-painted include the tear ducts. The advantage of this localized approach is that only affected regions are taken into consideration and information in other regions remain untouched as opposed to applying a median filter over the whole image [8].…”

Section: A Specular Reflectionsmentioning

confidence: 99%

Model-based pupil and iris localization

Mahadeo

Paplinski

Ray

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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The iris is the most accurate biometric to date and its localization is a vital step in any iris recognition system. Iris localization can be considered as the search for the demarcation points, or step change in intensity at its boundaries. A failed localization will lead to incorrect iris segmentation and eventually to poor recognition. In the first stage, we proceed with the elimination of reflection and the reduction of lighting variations in eye images. In the second stage of our proposed system, radii and locations of the pupil and iris are obtained by maximizing the convolution of the image with a toroidal 2-D filtering shape derived from the Petrou-Kittler 1-D filter. Such a novel approach delivers robust localization of the inner and outer iris boundaries. We tested our system on a large dataset of poor quality eye images with substantial occlusions, illumination and defocus and the proposed algorithm is found to be robust and accurate.

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Robust iris location in close-up images of the eye

Cited by 16 publications

References 14 publications

Iris Edge Extraction and Location by Advanced Watershed and Active Contour Method

Iris Edge Extraction and Location by Advanced Watershed and Active Contour Method

Cross Disciplinary Biometric Systems

Model-based pupil and iris localization

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