A Robust IRIS Segmentation Procedure for Unconstrained Subject Presentation

Zuo, Jinyu; Kalka, Nathan D.; Schmid, Natalia A.

doi:10.1109/bcc.2006.4341623

Cited by 72 publications

(27 citation statements)

References 8 publications

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“…[1][2] Abhyankar and Schuckers et al [7] proposed active shape model. Zuo et al [8] used Randomized Elliptical Hough Transform Weighted Integro-differential operator. Kennell et al [10] made use of Integro-differential operator, morphological operator for Image Binarization based on pixels and neighbourhood varaiance form fitting.…”

Section: Related Workmentioning

confidence: 99%

Iris Segmentation using Geodesic Active Contour for Improved Texture Extraction in Recognition

Pawar¹,

Lokhande²

2012

IJCA

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Automatic identification/verification of a person through biometrics has been getting extensive attention due to an increasing importance of security. The most popular biometric authentication scheme employed for the last few years is Iris Recognition. The performance of iris recognition system highly depends on segmentation. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented accurately. The iris proposed recognition module consists of the preprocessing system, segmentation, feature extraction and recognition. Mainly it focuses on image segmentation using Geodesic Active Contours and comparison with traditional methods of segmentation. As active contours can 1) assume any shape and 2) segment multiple objects at the same time, they lessen some of the concerns related with conventional iris segmentation models. The iris texture is extracted in an iterative fashion by considering both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA (Institute of Automation, Chinese Academy of Sciences) Interval version3 iris databases implemented in MATLAB shows the efficiency of the proposed technique application.

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Section: Related Workmentioning

confidence: 99%

Iris Segmentation using Geodesic Active Contour for Improved Texture Extraction in Recognition

Pawar¹,

Lokhande²

2012

IJCA

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“…Kong and Zhang proposed a model for detecting eyelashes and specular reflections [9]. Zou et al proposed a procedure for iris occlusion estimation [10]. In [11], Krichen et al proposed a probabilistic approach for iris quality measure.…”

Section: Previous Work -mentioning

confidence: 99%

Recognition of Human Iris Using Accurate Iris Map

Priyadharsini¹,

Saminathan²

2014

IOSRJCE

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“…Various forms of IDO and HT (Wildes, 1997, Zuo et al, 2006, Liu et al, 2005, Jan et al, 2013, Tan et al, 2010 are proposed in the literature. Apart from these two techniques, other boundary based methods found in the literature are, active shape models (Abhyankar and Schuckers, 2006), binary morphology and image statistics (Kennell et al, 2006), adaptive binarisation (Basit and Javed, 2007), Adaboost cascading and elastic model (He et al, 2009), Geodesic active counter (Shah and Ross, 2009), and eyeball model (Baek et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Shekar

Bhat²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor's series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

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A Robust IRIS Segmentation Procedure for Unconstrained Subject Presentation

Cited by 72 publications

References 8 publications

Iris Segmentation using Geodesic Active Contour for Improved Texture Extraction in Recognition

Iris Segmentation using Geodesic Active Contour for Improved Texture Extraction in Recognition

Recognition of Human Iris Using Accurate Iris Map

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

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