&lt;title&gt;A fast and robust iris localization method based on texture segmentation&lt;/title&gt;

Cui, Jiali; Wang, Yunlong; Tan, Tieniu; Ma, Li; Sun, Zhenan

doi:10.1117/12.541921

Cited by 115 publications

(71 citation statements)

References 19 publications

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“…Wildes [26] proposes iris segmentation through a gradient based binary edge map construction followed by circular Hough transform. This methodology is the most widely used, being proposed with minor several variants in [3], [11], [14], [17], [19], [18] and [20]. [16] proposes one simple method based on thresholds and function maximization in order to obtain two ring parameters corresponding to iris inner and outer borders.…”

Section: Related Workmentioning

confidence: 99%

UBIRIS: A Noisy Iris Image Database

Proença

Alexandre

2005

Lecture Notes in Computer Science

395

232

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Abstract. This paper presents a new iris database that contains images with noise. This is in contrast with the existing databases, that are noise free. UBIRIS is a tool for the development of robust iris recognition algorithms for biometric proposes. We present a detailed description of the many characteristics of UBIRIS and a comparison of several image segmentation approaches used in the current iris segmentation methods where it is evident their small tolerance to noisy images.

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Section: Related Workmentioning

confidence: 99%

UBIRIS: A Noisy Iris Image Database

Proença

Alexandre

2005

Lecture Notes in Computer Science

395

232

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“…In Table 2 and Table 3, the detection rates are observed by eyes because there is no standard method for evaluating the localization results. Since the iris segmentation results on CASIA iris image database version 1.0 is shown in [5], the performance of our proposed method is compared with their method in Table 2 and Table 3. It shows that our proposed method is comparable with their method.…”

Section: Resultsmentioning

confidence: 99%

“…The radius of the pupil can be calculated from the circle area formula. Cui et al [5] decomposed the iris image using Haar wavelet before pupil localization. Modified Hough Transform was used to obtain the center and radius of pupil.…”

Section: Related Workmentioning

confidence: 99%

An effective segmentation method for iris recognition system

Yew

Tay

et al. 2008

5th International Conference on Visual Information Engineering (VIE 2008)

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Iris recognition has become a popular research in recent years due to its reliability and nearly perfect recognition rates. Iris recognition system has three main stages: image preprocessing, feature extraction and template matching. In the preprocessing stage, iris segmentation is critical to the success of subsequent feature extraction and template matching stages. If the iris region is not correctly segmented, the eyelids, eyelashes, reflection and pupil noises would present in the normalized iris region. The presence of noises will directly deteriorate the iris recognition accuracy. The proposed approach gives a solution for compensating all four types of noises to achieve higher accuracy rate. It consists of four parts: (a) Pupil is localized using thresholding and Circular Hough Transform methods. Experimental results show that the proposed approach has achieved high accuracy of 98.62%.

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“…Cui et al [8] extract the low frequency information of iris image by wavelet transform. They use Itg-Diff operator to segment iris.…”

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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<title>A fast and robust iris localization method based on texture segmentation</title>

Cited by 115 publications

References 19 publications

UBIRIS: A Noisy Iris Image Database

UBIRIS: A Noisy Iris Image Database

An effective segmentation method for iris recognition system

Efficient Iris Localization via Optimization Model

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