Fast iris localization algorithm on noisy images based on conformal geometric algebra

Ma, Lin; Li, Haifeng; Yu, Kaiben

doi:10.1016/j.dsp.2020.102682

Cited by 21 publications

(7 citation statements)

References 18 publications

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“…However, this method is easily susceptible to a localization error of the pupil region because it does not take into account the potential presence of regions with lower gray values than the pupil in the iris image. In the study by Ma et al [12], a localization method based on conformal geometry was proposed, which was similar to CHT. Firstly, the grayscale values of the whole image were divided into three values (0, 150, and 255) according to the set threshold.…”

Section: Introductionmentioning

confidence: 99%

Iris Localization Algorithm based on Effective Area

Zhang

Wang

2021

International Journal of Antennas and Propagation

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Iris localization is the most crucial part of the iris processing because its accuracy can directly affect the accuracy of biometric identification in subsequent steps. Yet, the quality of iris images may be sharply degraded due to interference from eyelashes and reflections during image acquisition, which can affect the localization accuracy adversely. To solve the problem, an iris localization algorithm based on effective area is proposed. First, YOLOv4 is used to crop the image to obtain the effective iris area, which is beneficial in improving the accuracy of subsequent localization. Furthermore, a method to remove reflective noise is proposed, which can effectively avoid the problem of noise interference in the process of inner boundary determination. Finally, aiming at the edge deviation caused by eyelashes, an outer boundary adjustment method is proposed. The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Iris Localization Algorithm based on Effective Area

Zhang

Wang

2021

International Journal of Antennas and Propagation

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“…More edges will be detected when the threshold set is low and vice versa. Assisted by adaptive thresholding, edges of human iris and pupil resulted from Sobel operators and three points are selected randomly to generate a circle for human eye localization [3]. With a high threshold value, we might miss subtle edges, or most of the time output undesired edge fragments.…”

Section: Related Workmentioning

confidence: 99%

“…In the application of image processing, machine vision, and computer vision, edge detection is one of the crucial steps in pre-processing stages for finding the boundaries of objects within an image, for instance detecting local discontinuities in pixels intensity or brightness for boundaries extraction [1]. Edge detection is widely implemented in the application of car's license plate detection [2], human face recognition through iris localization for eye tracking [3], synthetic aperture radar images to detect edges of ships, aircraft, terrain, meteorological forms and mobile vehicles [4], agricultural plant leaves recognition [5], and dehaze or deblurring method [6]. Furthermore, biomedical image, i.e.…”

Section: Introductionmentioning

confidence: 99%

Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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In computer vision, edge detection is a crucial step in identifying the objects' boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true nonedge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.

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“…However, these are costly and uncomfortable to wear, and the accuracy degrades in an uncontrolled environment. Webcam‐based iris centre localization has recently garnered significant attention due to its cost‐effectiveness and non‐intrusive nature (Ma et al, 2020). Iris has a unique and stable circular pattern with lower intensity than its surrounding regions.…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing these intrusive characteristics, most researchers localize the iris centre using handcrafted features. Therefore, circle fitting techniques such as circular HOG, circular geometric algebra (CGA) (Ma et al, 2020), ISF, rectangular‐intensity‐gradient (RIG) (Ahmad et al, 2022), and so forth, are used for iris detection. Gradient‐based methods use dot products between gradient and displacement vectors for iris centre localization.…”

Section: Introductionmentioning

confidence: 99%

A cascaded deep learning framework for iris centre localization in facial image

Ahmad,

Muhammad,

Yadav

et al. 2023

Expert Systems

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Accurate iris centre localization is crucial in many computer vision and facial biometric applications such as gaze estimation, human–computer interaction, iris recognition, and liveness detection. However, it is challenging in an uncontrolled environment due to variations like pose, scale, rotation, specular reflection, and image quality. Therefore, a cascaded deep learning framework for iris centre localization in facial images is proposed that is robust to the abovementioned variations. The proposed approach consists of (i) YOLOv3 for eye detection, (ii) UNet for iris segmentation, and (iii) statistical modelling for iris centre localization. The eyes are first detected using the YOLOv3, and subsequently, iris segmentation is performed within the detected eyes using the UNet. Following iris segmentation, statistical modelling is employed to enhance the localization accuracy of the iris centre. Experiments were performed on benchmark databases, resulting in a standardized error measure SED of 3.405 pixels for BioID and 3.259 pixels for GI4E databases. In addition, the robustness of the proposed eye detection model was further evaluated on the Yale B for illumination variations and the CAS‐PEAL for pose variations.

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Fast iris localization algorithm on noisy images based on conformal geometric algebra

Cited by 21 publications

References 18 publications

Iris Localization Algorithm based on Effective Area

Iris Localization Algorithm based on Effective Area

Mini Kirsch Edge Detection and Its Sharpening Effect

A cascaded deep learning framework for iris centre localization in facial image

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