Reliable iris localization using Hough transform, histogram-bisection, and eccentricity

Jan, Farmanullah; Usman, Imran; Agha, Shahrukh

doi:10.1016/j.sigpro.2012.07.033

Cited by 48 publications

(31 citation statements)

References 22 publications

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“…The published methods included in the comparison are chosen on the basis that they used same databases for experimentation that we have taken. Moreover, Jan et al [8], [9] show the highest accuracy among all the iris localization methods available in the literature for CITHV4 and CILV3 databases. The symbol --in the table shows that the corresponding information was not found in the literature.…”

Section: Comparison With Other Methodsmentioning

confidence: 97%

“…The Table 3 shows that the proposed algorithm has the highest accuracy and lowest time cost per image, which has happened due to the proposed edge map and the adaptive CHT used for pupil and limbic boundary detection respectively, as compared to the other methods in the table. In the proposed method, the original iris image is scaled down to half size, which was also done in the Jan et al methods [8], [9] to speed up the processing. The image resizing by a scaling factor, s = 0.5 not only reduces all the edge pixels to half in number, but also the number of radii taken in a CHT algorithm becomes half.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…However, their iris localization algorithms work under very controlled environments and they do not perform very accurately while dealing with the noisy images [6]. Some recent methods to localize irises in noisy NIR and VW images are described in [5], [7]- [9] and [10]- [12] respectively. Hough transform (HT) based iris localization algorithms consider the iris as a circular ring and the CHT is used to detect the circles as illustrated in [7], [10], [12].…”

Section: Introductionmentioning

confidence: 99%

“…The generating optimal edge maps get more challenging if the images are noisy such as CASIA-Iris-Thousand, version 4.0 (CITHV4) database [17] images. The noisy images are first preprocessed to remove the noise such as lighting reflections, non-uniform illumination and low contrast as described in [6]- [9], which improves the accuracy and time performance of the iris localization. The image inpainting techniques are used for removing the lighting reflection spots of the iris images and the histogram equalization is used for compensating the non-uniform illumination and low contrast [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

Kumar¹,

Asati²,

Gupta³

2016

IJECE

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This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIAIris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results.

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Section: Comparison With Other Methodsmentioning

confidence: 97%

Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

Kumar¹,

Asati²,

Gupta³

2016

IJECE

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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 Fast and Accurate Pupil Localization Method Using Gray Gradient Differential and Curve Fitting

Lin

Zhang

et al. 2015

Proceedings of the 4th International Conference on Computer Engineering and Networks

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Pupil localization plays a key role in recognizing the biological characteristics of iris that is self-evident. In recent years, the most common procedures to localize the pupil have been based on the edge detector and circle finder, such as integro-differential operator and Hough transform. However, the circle finder overemphasizes geometric characteristics, which reduces the accuracy and real-time performance under complex conditions. In this chapter, a new threshold method using discrete gray gradient differentials based on the unique features of gaps in the gray gradients of pupil boundaries is proposed to binarization first. We then highlight the geometric characteristics of the pupil by adopting the strategy of combining outline filling with curve fitting to locate pupil boundaries. Compared to the Wildes system, the proposed method is feasible, fast, accurate, and stable.

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Reliable iris localization using Hough transform, histogram-bisection, and eccentricity

Cited by 48 publications

References 22 publications

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

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

A Fast and Accurate Pupil Localization Method Using Gray Gradient Differential and Curve Fitting

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