An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

Malinowski, K.; Saeed, Khalid

doi:10.1016/j.bbe.2022.02.010

Cited by 13 publications

(6 citation statements)

References 36 publications

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“…Many works are devoted to the specific problem of iris detection, for example [10][11][12][13][14][15]. At the input, the information of the original image is taken without prior hierarchical or other ordering.…”

Section: Methods Demonstrationmentioning

confidence: 99%

Example-Based Object Detection in the Attached Image

Kharinov

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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The paper solves the problem of detecting exemplified objects in a color image. A solution provides the representation of similar objects in the same colors, and different objects in different colors. This is achieved by combining images of object examples and a target image into a single joint image, which is represented in sequential number 1, 2, ..., etc. colors. The mentioned effect is demonstrated by detecting irises and pupils in a test image. It is explained by the fact that: a) the joint image is approximated by a hierarchy of approximations in sequential color numbers; b) the hierarchy of approximations is described by a convex sequence of approximation errors (values of the total squared error ); c) due to the convexity, the approximation errors are reduced for all approximations of the joint image. In the last explanation item, it is applied the operation of combining hierarchically organized objects into a single object, which is introduced in this paper. To produce the required hierarchy of image approximations Ward's pixel clustering is used. Ward's method is generalized for image processing by parts (within pixel subsets) that provides generation of multiple proper approximation hierarchies and accelerates the calculations. To do so, the so- called split-and-merge pixel cluster CI-method is embedded into Ward's generalized method to provide a real-life minimization of the error for image approximation in a fixed number of colors.

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Section: Methods Demonstrationmentioning

confidence: 99%

Example-Based Object Detection in the Attached Image

Kharinov

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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“…In our two previous articles [5][6] we investigated the problem of correctly extracting the iris from heavily noisy images. During our research, we faced the problem of extracting the pupil area and determining the outer boundary of the iris.…”

Section: Related Workmentioning

confidence: 99%

Real-time siamese neural network based algorithm for user recognition by their eye blinking

Malinowski,

Saeed

2024

Preprint

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The article proposes a new method for user recognition based on a unique eyelid blinking pattern. Our research aimed to develop a user recognition method using eyelid blinking that is resistant to shoulder surfing and brute force attacks, while also not requiring complex recording devices. Most user authentication methods utilizing eyelid blinking patterns are vulnerable to pattern replication attacks. On the other hand, methods using EEG sometimes require the use of complicated equipment to record the blinking event. In our study, we utilized the publicly available mEBAL database. The temporal eyelid movement patterns extracted from the samples in the database are analyzed by a Siamese neural network. Our achieved results of 98.20% accuracy and 0.11 EER unequivocally demonstrate the superiority of the proposed method over other methods using eyelid blinking for user authentication.

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“…Assuming that the interval distance is G, the planes are drawn using Eqs. ( 4)- (6). Then the multiple cubes with side length G are constructed as…”

Section: Definition Of Effective Density Blockmentioning

confidence: 99%

“…1,2 The detection of circular objects in images is a basic problem in computer vision and pattern recognition. 3 Circle detection is widely used in the fields of spacecraft, 4 industrial component measurements, 5 iris localization, 6,7 nanoparticle research, 8 cell analysis, 9,10 robotics, 11 traffic signs, 12 and automatic statistics of steel bars. 13 Therefore, the research on the circle detection method is of great practical significance and value.…”

Section: Introductionmentioning

confidence: 99%

Circle detection algorithm based on neighborhood density clustering

Li,

Wang,

Zhang

et al. 2023

J. Electron. Imag.

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.Circle detection in images is one of the key technologies in machine vision, pattern recognition, and artificial intelligence. However, conventional circle detection methods are sensitive to complex scenes, noise, and occlusion in images. Solving the impact of these situations is still the focus of circle detection algorithm research. Therefore, a circle detection algorithm based on neighborhood density clustering (NDC) is proposed. The proposed algorithm calculates circle parameters of connected regions after extracting corners, corroding the corners, and marking the connected regions. Then, NDC of the circle parameters is executed to classify arcs belonging to the same circle into one category to acquire a virtual connected region. And the circle parameters of the virtual connected region are clustered using NDC again to obtain the circle parameter dataset of the circle to be detected. The precise circle parameters are further estimated by calculating the centroid of each category. To prevent false positives, candidate circles are verified through a ratio rule. Extensive experiments using both synthetic and real images were performed. The results compared with those of representative state-of-the-art methods demonstrate that the proposed algorithm can be applied to a variety of complex scenes and has several advantages: good anti-occlusion effect, more robustness against noise, high accuracy, and better performance.

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An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

Cited by 13 publications

References 36 publications

Example-Based Object Detection in the Attached Image

Example-Based Object Detection in the Attached Image

Real-time siamese neural network based algorithm for user recognition by their eye blinking

Circle detection algorithm based on neighborhood density clustering

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