Circular Hough Transform for Iris localization

Cherabit, Noureddine; Chelali, Fatma Zohra; Djéradi, Amar

doi:10.5923/j.scit.20120205.02

Cited by 79 publications

(23 citation statements)

References 10 publications

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“…The accuracy of the pupil localization could be measured by calculating the percentage of the eye pupil images for which the pixel error was lower than a threshold in pixels. We compared our pupil detection method with the classical Starburst [33] algorithm and circular Hough transform (CHT) [36]. The performance of pupil localization with different algorithms is illustrated in Table 3.…”

Section: Results Of Pupil Localizationmentioning

confidence: 99%

Intelligent Evaluation of Strabismus in Videos Based on an Automated Cover Test

Zheng

Hong

et al. 2019

Applied Sciences

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Strabismus is a common vision disease that brings about unpleasant influence on vision, as well as life quality. A timely diagnosis is crucial for the proper treatment of strabismus. In contrast to manual evaluation, well-designed automatic evaluation can significantly improve the objectivity, reliability, and efficiency of strabismus diagnosis. In this study, we have proposed an innovative intelligent evaluation system of strabismus in digital videos, based on the cover test. In particular, the video is recorded using an infrared camera, while the subject performs automated cover tests. The video is then fed into the proposed algorithm that consists of six stages: (1) eye region extraction, (2) iris boundary detection, (3) key frame detection, (4) pupil localization, (5) deviation calculation, and (6) evaluation of strabismus. A database containing cover test data of both strabismic subjects and normal subjects was established for experiments. Experimental results demonstrate that the deviation of strabismus can be well-evaluated by our proposed method. The accuracy was over 91%, in the horizontal direction, with an error of 8 diopters; and it was over 86% in the vertical direction, with an error of 4 diopters.

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Section: Results Of Pupil Localizationmentioning

confidence: 99%

Intelligent Evaluation of Strabismus in Videos Based on an Automated Cover Test

Zheng

Hong

et al. 2019

Applied Sciences

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“…Among these entire techniques,CHT algorithm that explained in [8], [9]achieves higher recognition accuracies with allCASIA-Iris V3 images, and it can robust in the presence of blurring and noise in image. CHT works in the parameter space because we can locate the edge points of iris in parameter space [8].On the other hand, there are two problems in CHT, first, it requires threshold values for edge detection and it has a heavy computational cost.…”

Section: I In Nt Tr Ro Od Du Uc Ct Ti Io On Nmentioning

confidence: 99%

Noisy Iris Recognition Based on Deep Neural Network

Omran

Soliman

Salah

et al. 2020

Menoufia Journal of Electronic Engineering Research

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Iris recognition is one of the Biometric systems used for persons identification based on their special iris traits, which are unique featuresfor each individual. It is clear that the progress in deep learning show how efficient the extracted features from convolutional neural networks (CNNs) to describe the complex image patterns. However, the influence of noise is a serious problem in most image processing systems. It may ariseto the iris recognition systems due to environmental conditions that can affects the features extracted from the iris images. Hence, the objective of this paper is to study the performance of CNNs based Deep learning (Alex net, Vgg16 and Vgg19) when used for iris recognition with the presence of noise and compares it with Masek algorithm. Simulation results reveal that using the deep learning greatly improves iris recognition accuracy for Alex CNN. We achieve 100%, 100%, 88.9% for interval, lamp and twins datasets respectively.

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“…Where r is the radius, (a,b) is the center of the circle, and (x,y) represents the circle coordinates [13]. The output of canny edge detection is shown in Fig.…”

Section: B Segmentationmentioning

confidence: 99%

Iris Matching Step Implementation in FPGA

Farhan¹,

Al-Gailani²

2019

Iraqi Journal of ICT

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Iris recognition system is broadly being utilized as it has distinctive patterns that gives it a powerful strategy to distinguish between persons for identification purposes. However, this system in this implementation requires large memory capacity and high computation time. These factors make us in a challenge to find a way to run this algorithm in a hardware platform. The hardware implementation features reduce the execution time by exploiting the parallelism and pipeline. The present work addresses this issue when reducing execution time by implementing the matching step using hamming distance algorithm on the target device FPGA KINTEX 7 using Xilinx system generator. The obtained result demonstrates that the execution time has been accelerated to 1.32 ns, which is almost at least four times faster than existing works

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Circular Hough Transform for Iris localization

Cited by 79 publications

References 10 publications

Intelligent Evaluation of Strabismus in Videos Based on an Automated Cover Test

Intelligent Evaluation of Strabismus in Videos Based on an Automated Cover Test

Noisy Iris Recognition Based on Deep Neural Network

Iris Matching Step Implementation in FPGA

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