A Robust Iris Localization Method Using an Active Contour Model and Hough Transform

Koh, Jaehan; Govindaraju, Venu; Chaudhary, Vipin

doi:10.1109/icpr.2010.699

Cited by 65 publications

(13 citation statements)

References 11 publications

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“…Several fundamental ideas were unveiled in his initial publication, such as a simple but effective algorithm for locating circular approximations to the inner and outer iris boundaries, the notion of "unwrapping" an iris into a dimensionless polar coordinate system, and the use of Gabor filters to computationally analyze iris texture. Since then, many others have published work attempting to improve various parts of the iris recognition procedure [22] [23] [24] [25] [26]. A very good summary of the history of iris recognition and the various published works can be found in Bowyer's survey paper [27].…”

Section: Relevant Prior Workmentioning

confidence: 99%

Computing Cyclotorsion in Refractive Cataract Surgery

2016

IEEE Trans. Biomed. Eng.

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Section: Relevant Prior Workmentioning

confidence: 99%

Computing Cyclotorsion in Refractive Cataract Surgery

2016

IEEE Trans. Biomed. Eng.

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“…Sundaram et al [9] reduce search range of iris and pupil to reduce complexity; then they utilize Hough transform for localization. Other algorithms such as Hooke's Law [10], active contour [11,12], and histogram analysis [13] also get satisfying results. More iris segmentation methods are surveyed by Jan [14].…”

Section: (3) Incorrect Segmentation Of the Upper And Lower Eyelidsmentioning

confidence: 99%

Efficient Iris Localization via Optimization Model

Wang

Liu

Shu

et al. 2017

Mathematical Problems in Engineering

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Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

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“…Different researchers exploited several iris feature extraction techniques, including Gabor wavelets, Daubechies wavelets, and discrete cosine transform [1]- [7]. Local Binary Pattern (LBP) has been regarded as one of the most robust local texture descriptor, and it has been shown promising performance in the computer vision field, including motion analysis and face recognition [10].…”

Section: Introductionmentioning

confidence: 99%

“…The modified Mumford-Shah segmentation model was deployed in [2] to localize the iris and pupil boundary. A traditional active contour model and the Hough transform technique were applied to segment the iris region in [7]. The Geodesic Active Contours (GAC) were employed in [8] to isolate iris information from the off-angle iris images.…”

Section: Introductionmentioning

confidence: 99%

Iris Recognition Using Fuzzy Level Set and GEFE

O'Connor¹,

Roy²,

Shelton³

et al. 2014

IJMLC

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Abstract-This paper presents an efficient algorithm for iris recognition using the spatial fuzzy clustering with level set method, and genetic and evolutionary feature extraction techniques. The novelty of this research effort is that we deploy a fuzzy c-means clustering with level set (FCMLS) method in an effort to localize the nonideal iris images accurately. The FCMLS method incorporates the spatial information into the level set-based curve evolution approach and regularizes the level set propagation locally. The proposed iris localization scheme based on FCMLS avoids the over-segmentation and performs well against blurred iris/sclera boundary. Furthermore, we apply a genetic and evolutionary feature extraction (GEFE) technique, which uses genetic and evolutionary computation to evolve modified local binary pattern (MLBP) feature extractor to elicit the distinctive features from the unwrapped iris images. The MLBP algorithm combines the sign and magnitude features for the improvement of iris texture classification performance. The identification and verification performance of the proposed scheme is validated using the CASIA version 3 interval dataset.Index Terms-Iris recognition, fuzzy c-means clustering, level set, modified local binary pattern, and genetic and evolutionary feature extraction.

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A Robust Iris Localization Method Using an Active Contour Model and Hough Transform

Cited by 65 publications

References 11 publications

Computing Cyclotorsion in Refractive Cataract Surgery

Computing Cyclotorsion in Refractive Cataract Surgery

Efficient Iris Localization via Optimization Model

Iris Recognition Using Fuzzy Level Set and GEFE

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