Accurate iris segmentation based on novel reflection and eyelash detection model

Kong, Wai-Kin; Zhang, Dingguo

doi:10.1109/isimp.2001.925384

Cited by 184 publications

(108 citation statements)

References 5 publications

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“…The strong reflection area is identified, in the first stage of segmentation, based on the method reported in [16]. A simple thresholding approach is applied to find the strong reflection area.…”

Section: Segmentation Of Iris Imagesmentioning

confidence: 99%

“…A pixel with an intensity value higher than a certain threshold belongs to the strong reflection area. The reflection is found by the following inequality [16]: (1) where denotes the intensity of an image at the point and is the required threshold value. A weak reflection area denotes the transition from a strong reflection to the iris region.…”

Section: Segmentation Of Iris Imagesmentioning

confidence: 99%

“…A weak reflection area denotes the transition from a strong reflection to the iris region. We apply the following statistical inequality to detect the weak reflection points [16]: (2) where and are the mean and standard deviation for the distribution of the intensity in the iris image respectively, and denotes the control parameter. Any pixel that is close to the strong reflection area and that satisfies (2) is considered as a weak reflection.…”

Section: Segmentation Of Iris Imagesmentioning

confidence: 99%

“…Profiting from the flexibility in (17), the fuzzy clustering results can certainly improve the LS evolution process. If the intriguing factor in fuzzy clustering results produce [9], we can find it advantageous to start the LS function as (18) where is a constant and used to adjust the Dirac function as defined in (16). Bk is a binary image retrieved from (19) where is an adjustable threshold.…”

Section: International Journal Of Machine Learning and Computing Volmentioning

confidence: 99%

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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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Section: Segmentation Of Iris Imagesmentioning

confidence: 99%

Section: Segmentation Of Iris Imagesmentioning

confidence: 99%

Section: Segmentation Of Iris Imagesmentioning

confidence: 99%

Section: International Journal Of Machine Learning and Computing Volmentioning

confidence: 99%

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Iris Recognition Using Fuzzy Level Set and GEFE

O'Connor¹,

Roy²,

Shelton³

et al. 2014

IJMLC

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“…In the final step, we apply the DRLS [7] to find the exact outer boundary of the iris based on the estimated pupil boundary obtained in the previous step. In the first stage of segmentation, we detect the reflection areas using a simple thresholding technique [15]. Fig.…”

Section: Iris Segmentation Using Drlsmentioning

confidence: 99%

Iris Recognition Using Level Set and Local Binary Pattern

O'Connor¹,

Roy²

2014

IJCTE

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Abstract-This paper presents an efficient algorithm for iris recognition using the Level Set (LS) method and Local Binary Pattern (LBP). We deploy a Distance Regularized Level Set (DRLS)-based iris segmentation procedure in which the regularity of the Level Set (LS) function is intrinsically maintained during the curve propagation process. The LS evolution is derived as the gradient flow that minimizes energy functional with a distance regularization term and an external energy that drives the motion of the zero LS toward iris boundary accurately. DRLS also uses relatively large time steps in the finite difference scheme to reduce the curve propagation time. The deployed variational model is robust against poor localization and weak iris/sclera boundaries. Furthermore, we apply a Modified LBP (MLBP) in an effort to elicit the iris feature elements. The MLBP combines both 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, distance regularized level set, modified local binary pattern. I. INTRODUCTIONMost current iris recognition algorithms perform relatively well in a strictly controlled environment. However, the iris images captured in a noisy environment produce nonideal iris images with a varying image quality and are severely affected from eyelid and eyelash occlusions, motion blurs, camera diffusions, head rotations, gaze directions, camera angles, and reflections [1]. Furthermore, the iris contours of the noisy irises are not exactly circular or elliptical and may be of any kind of shapes [2]- [6]. To mitigate the shape irregularities of the iris contours, several researchers proposed different iris segmentation methods based on active contours, including the modified Mumford-Shah segmentation model [2], Variational Level Set (VLS) [3], Fourier series expansions of the contour data [4], and Geodesic Active Contours (GAC) [5].The segmentation approaches based on the traditional active contours may involve a huge computational time. Also, the parametric active contour-based iris segmentation scheme may be terminated due to specular reflections, the Manuscript received October 30, 2013; revised February 17, 2014 thick radial fibres in the iris and the crypts in the ciliary region. In conventional active contour method with the LS formulation, the LS function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution [7]. Addressing the above problems, we apply the Distance Regularized Level Set (DRLS) method, proposed in [7], to localize the iris contour. In the DRLS method, the regularity of the LS function is intrinsically maintained during the curve propagation. The LS evolution is derived as the gradient flow that minimizes energy functional with a distance regularization term and an external energy that drives the motion of t...

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UBIRIS: A Noisy Iris Image Database

Proença

Alexandre

2005

Lecture Notes in Computer Science

393

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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Accurate iris segmentation based on novel reflection and eyelash detection model

Cited by 184 publications

References 5 publications

Iris Recognition Using Fuzzy Level Set and GEFE

Iris Recognition Using Fuzzy Level Set and GEFE

Iris Recognition Using Level Set and Local Binary Pattern

UBIRIS: A Noisy Iris Image Database

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