Non-ideal iris segmentation using graph cuts

Pundlik, Shrinivas; Birchfield, Stan

doi:10.1109/cvprw.2008.4563108

Cited by 72 publications

(39 citation statements)

References 12 publications

(10 reference statements)

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“…Our approach bases on the region-shrinking technique described in [9], but instead of defining a hard threshold by hand, we find it through modal analysis on the histogram of intensities. Initially, all masked pixels are listed as non-valid.…”

Section: Removal Of Specularitiesmentioning

confidence: 99%

A novel method for low-constrained iris boundary localization

Fernández¹,

Pérez²,

Segura³

et al. 2012

2012 5th IAPR International Conference on Biometrics (ICB)

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Section: Removal Of Specularitiesmentioning

confidence: 99%

A novel method for low-constrained iris boundary localization

Fernández¹,

Pérez²,

Segura³

et al. 2012

2012 5th IAPR International Conference on Biometrics (ICB)

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“…2.1. (2003) Geodesic active contour Roy et al (2007) Variational level sets Daugman (2003) Fourier-based approximation Schuckers et al (2007) Active shape models Pundlik et al (2008) Graph cuts Zuo and Schmid (2010) Ellipse fitting He et al (2013) Pulling and pushing model Du et al (2014) Scale invariants feature transform Masek et al (2015; Circular Hough transform Lim et al, 2014;Huang et al, 1990;Yuan et al (2005) Edge detection and Hough transform Huang et al (1990) Phase congruency and Hough transform Based on available documented literatures, Jillela and Ross (2013) had reported there is various iris segmentation approaches have been proposed to deal with the non-ideal eye image such as off-angled, occlusion and blurry motion situation as summarizes in Table 2.2. However, it is very rare to find documented publications on pigment spots.…”

Section: ) **Na: Not Availablementioning

confidence: 99%

Implementation of Geodesic Active Contour Approach for Pigment Spots Segmentation on the Iris Surface

Man

Jabal

Rahim

et al. 2016

Journal of Computer Science

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Abstract:The aim of this paper is to study the effectiveness of geodesic active contour approach to segment the pigment spots on iris surface. Pigment spot is an abnormal marker that appears on the iris surface, which may lead to eye diseases. The multi appearance, scattered location and dynamic shape of the pigment spots on the iris surface make them very difficult to detect using automatic segmentation method. Therefore, this paper presents the preliminary works that observe the efficiency and accuracy of geodesic active contour approach to automatically segment the pigment spots on iris surface. Miles database and "lu_wei_feb_2006" dataset are used in this study. The results of the conducted experiment show that geodesic active contour approach able to correctly and accurately detect up to 77% and 90% of the pigment spots in Miles database and "lu_wei_feb_2006" dataset, respectively.

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“…IPFv (vertical integral projection function) and IPFh (horizontal integral projection function) that can be reformulated by the equations (4) and (5): (6) and (7).…”

Section: Angular Integral Projection Function (Aipf)mentioning

confidence: 99%

“…Wildes and Masek [5,6] used edge detection followed by the Hough transforms to detect the iris boundaries and both are circle models. Another proposition assumes the iris and the pupil as an elliptic form [7,8]. However, these methods are not so good in particular for noisy, blurred and off-angle irises.…”

Section: Introductionmentioning

confidence: 99%

Active Contour without Edges vs GVF Active Contour for Accurate Pupil Segmentation

Aydi¹,

Nouri²,

Kamoun³

2012

IJCA

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Iris localization is a critical step for an iris recognition system because it directly affects the recognition rates. Consequently, in order to have reasonably accurate measures, we should estimate as many iris boundaries as possible which are defined by papillary and ciliary regions. Due to the contraction which is an intrinsic propriety of the pupil and the variations in the shooting angle, the pupil will not be a regular circle. So an active contour is suitable to accurately locate the iris boundaries. In this paper we focused on iris/pupil boundary and we proposed a new algorithm based on an active contour without edges applied in gray level image. First, we develop a new method to locate and fill the corneal reflection which is used not only to remove the highlight points that appear inside the pupil but also as an initial contour generator for the snake. Second, we propose to use the active contour without edges for precise pupil segmentation. This kind of snake can detect objects whose boundaries are not necessarily defined by gradient. Our algorithm seems to be robust to occlusion, specular reflection, variation in illumination and improves its efficiency in precision and time computation compared with AIPF and Gvf active contour. Another advantage is that the initial curve can be anywhere in the image and the contour will be automatically detected.The proposed algorithm is 2.36 faster than GVF snake-based method for accurate pupil contour detection and integrodifferential method with accuracy up to 99.62% using CASIA iris database V3.0 and up to 100% with CASIA iris database V1.0.

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Non-ideal iris segmentation using graph cuts

Abstract: Abstract

Cited by 72 publications

References 12 publications

A novel method for low-constrained iris boundary localization

A novel method for low-constrained iris boundary localization

Implementation of Geodesic Active Contour Approach for Pigment Spots Segmentation on the Iris Surface

Active Contour without Edges vs GVF Active Contour for Accurate Pupil Segmentation

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