On Improvement for Normalizing Iris Region for a Ubiquitous Computing

Joung, Bong Jo; Chung, Chin Hyun; Lee, Key Seo; Yim, Wha Young; Lee, Sang Hyo

doi:10.1007/11424758_128

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…From the perspective of iris texture feature extraction, the normalization techniques are classified into six categories such as Linear model (Daugman 1993 ; Lim et al 2001 ; Joung et al 2005 ; Boles and Boashash 1998 ; Ma et al 2003 ; Subbarayudu and Prasad 2008 ; Shamsi and Rasouli 2009 , 2011 ), variant of linear model (Krishnamoorthi et al 2012 ), non-linear model (Wildes 1997 ; Wyatt 2000 ), the combination of non-linear and linear-models (Wei et al 2007 ; Yuan and Shi 2005 ), non-polar coordinate normalization (Arvachech and Tizhoosh 2006 ) and irregular border normalization (Han et al 2009 ; Shah and Ross 2009 ).…”

Section: Review Of Literaturementioning

confidence: 99%

“…They have normalized the distance between the pupil border and the limbus border into [0, 60] according to the arbitrary radius r and normalized the angular resolution into [0, 450] according to the step angle 0.8°. Joung et al ( 2005 ) have unwrapped the iris with limbus center to define the polar coordinates of the points over the limbus border and used pupil center to define the polar coordinates of the pupil border. The coordinates of the other points between these two borders are obtained linearly in the radial direction.…”

Section: Review Of Literaturementioning

confidence: 99%

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Trade off between variable and fixed size normalization in orthogonal polynomials based iris recognition system

Krishnamoorthi

Poorani

2016

SpringerPlus

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Iris normalization is an important stage in any iris biometric, as it has a propensity to trim down the consequences of iris distortion. To indemnify the variation in size of the iris owing to the action of stretching or enlarging the pupil in iris acquisition process and camera to eyeball distance, two normalization schemes has been proposed in this work. In the first method, the iris region of interest is normalized by converting the iris into the variable size rectangular model in order to avoid the under samples near the limbus border. In the second method, the iris region of interest is normalized by converting the iris region into a fixed size rectangular model in order to avoid the dimensional discrepancies between the eye images. The performance of the proposed normalization methods is evaluated with orthogonal polynomials based iris recognition in terms of FAR, FRR, GAR, CRR and EER.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Trade off between variable and fixed size normalization in orthogonal polynomials based iris recognition system

Krishnamoorthi

Poorani

2016

SpringerPlus

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Tuning Iris Recognition for Noisy Images

Ferreira

Lourenço

Pinto

et al. 2010

Biomedical Engineering Systems and Technologies

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The use of iris recognition for human authentication has been spreading in the past years. Daugman has proposed a method for iris recognition, composed by four stages: segmentation, normalization, feature extraction, and matching. In this paper we propose some modifications and extensions to Daugman's method to cope with noisy images. These modifications are proposed after a study of images of CASIA and UBIRIS databases. The major modification is on the computationally demanding segmentation stage, for which we propose a faster and equally accurate template matching approach. The extensions on the algorithm address the important issue of pre-processing that depends on the image database, being mandatory when we have a non infra-red camera, like a typical WebCam. For this scenario, we propose methods for reflection removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images show that the template matching segmentation method is more accurate and faster than the previous one, for noisy images. The proposed algorithms are found to be efficient and necessary when we deal with non infra-red images and non uniform illumination.

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