Localization of noncircular iris boundaries using morphology and arched Hough transform

Ghodrati, Hamed; Dehghani, Mohammad Javad; Helfroush, Mohammd Sadegh; Kazemi, Kamran

doi:10.1109/ipta.2010.5586780

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The aggregation operators, quantified majority operator (QMA-OWA) [ 16 ], were used to attain iris circular boundaries. Ghodrati et al [ 17 ] proposed a localization algorithm and applied a set of morphological operators, canny edge detector [ 18 ], and Hough transforms. Wang and Xiao [ 19 ] proposed an iris segmentation approach that relies on the difference operator of radial directions.…”

Section: Related Workmentioning

confidence: 99%

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

Putri

Aslam

et al. 2021

Sensors

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Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.

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Section: Related Workmentioning

confidence: 99%

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

Putri

Aslam

et al. 2021

Sensors

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“…Betancourt and Silvente [23] obtained circular boundaries using QMA-OWA operators [24]. Ghodrati et al [25] used a set of morphological operators, canny edge detector [26], and Hough transforms. Wang and Xiao [27] constructed a difference operator of radial directions.…”

Section: Background Of Iris Segmentationmentioning

confidence: 99%

An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

Huang

Juan

2019

Mobile Information Systems

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Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points detected by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. In this paper, we present a combination method of learning-based and edge-based algorithms for iris segmentation. A well-designed Faster R-CNN with only six layers is built to locate and classify the eye. With the bounding box found by Faster R-CNN, the pupillary region is located using a Gaussian mixture model. Then, the circular boundary of the pupillary region is fit according to five key boundary points. A boundary point selection algorithm is used to find the boundary points of the limbus, and the circular boundary of the limbus is constructed using these boundary points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.

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“…Good results in iris segmentation are reported but the number of parameters to adjust and search increased (major and minor axis length, center coordinates and rotation angle). In [13] intensity information is used to find a square region that completely surrounds the pupil. The square region is then binarized to extract an edge map.…”

Section: Literature Reviewmentioning

confidence: 99%

Fast and Accurate Pupil Isolation Based on Morphology and Active Contour

Abdullah¹,

Dlay²,

Woo³

2014

IJIEE

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The performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non-ideal iris images. This paper proposes a fast and accurate method to segment the pupil regardless of the shape using a combination of morphological operations and snake active contour. The morphological operations are used to determine a rough boundary of the pupil region while the active contour is used to find the precise boundary. The proposed scheme is robust in finding the exact pupil boundary of non-ideal iris image. Experimental results on the CASIA version 4.0 iris databases have indicated a high level of accuracy using the proposed technique.

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Localization of noncircular iris boundaries using morphology and arched Hough transform

Cited by 6 publications

References 14 publications

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

Fast and Accurate Pupil Isolation Based on Morphology and Active Contour

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