Search Results - (( simulation optimization problems algorithm ) OR ( iris segmentation using algorithm ))
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1
Adaptive Initial Contour and Partly-Normalization Algorithm for Iris Segmentation of Blurry Iris Images
Published 2022“…This algorithm is significant to solve inaccurate segmentation on blurry iris images.…”
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Black hole algorithm along edge detector and circular hough transform based iris projection with biometric identification systems
Published 2024“…The iris boundary is discovered using the suggested segmentation approach and a computational model of the pixel value. …”
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DEVELOPMENT OF IRIS RECOGNITION SYSTEM
Published 2016“…Hough transform method is used in iris segmentation to detect location of iris and pupil region in the image. …”
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Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System
Published 2012“…Iris segmentation is foremost part of iris recognition system. …”
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Iris segmentation and normalization approach
Published 2008“…The algorithm is tested using iris images from CASIA database and MMU database. …”
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Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System
Published 2012“…Iris segmentation is foremost part of iris recognition system.There are four steps in iris recognition: segmentation,normalization,encoding and matching.Here, iris segmentation has been implemented using Hough Transform and IntegroDifferential Operator techniques.The performance of iris recognition system depends on segmentation and normalization technique.Iris recognition systems capture an image from individual eye.Then the image captured is segmented and normalized for encoding process.The matching technique,Hamming Distance,is used to match the iris codes of iris in the database weather it is same with the newly enrolled for verification stage.These processes produce values of average circle pupil,average circle iris,error rate and edge points.The values provide acceptable measures of accuracy False Accept Rate (FAR) or False Reject Rate (FRR).Hough Transform algorithm,provide better performance,at the expense of higher computational complexity.It is used to evolve a contour that can fit to a non-circular iris boundary.However,edge information is required to control the evolution and stopping the contour.The performance of Hough Transform for CASIA database was 80.88% due to the lack of edge information.The GAR value using Hough Transform is 98.9% genuine while 98.6% through Integro-Differential Operator.…”
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Eye closure and open detection using Adaptive Thresholding Histogram Enhancement (ATHE) technique and connected components utilisation
Published 2014“…Eye closure detection is an important operation prior to carry out the main algorithm such as iris recognition algorithms, and eye tracking algorithms. …”
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An efficient iris image thresholding based on binarization threshold in black hole search method
Published 2018“…Several algorithms have been proposed in order to segment the outer and lower boundary of the iris region. …”
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Development of efficient iris identification algorithm using wavelet packets for smartphone application
Published 2017“…Performance of the proposed algorithm was tested on Chinese Academy of Sciences Institute of Automation (CASIA) iris image database. …”
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Iris Recognition As Biometric Authentication
Published 2016“…Biometric authentication system utilises the behavioural and physical traits to identify individuals. In this project, an iris recognition technique based on the John Daugman’s algorithm is used. …”
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Final Year Project -
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Feature Extraction From Epigenetic Traits Using Edge Detection In Iris Recognition System
Published 2013“…Most iris recognition systems use algorithms developed by Daugman. …”
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Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The segmentation iris is transformed to rectangular shape using the Rubber Sheet method. …”
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A discrete simulated kalman filter optimizer for combinatorial optimization problems
Published 2022“…Another type of algorithm is called numerical algorithms. These algorithms were built specifically to address numerical optimization problems. …”
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Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
Published 2016“…To evaluate the performance of the Simulated Kalman Filter algorithm, it is applied to 30 benchmark functions of CEC 2014 for real-parameter single objective optimization problems. …”
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Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems
Published 2016“…However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve discrete optimization problems, the SKF algorithm is combined with an angle modulated approach. …”
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Model of Bayesian tangent eye shape for eye capture
Published 2014“…Using the algorithms proposed by J. Daugman, Iris recognition system has significantly improved over the last decade, and it has been used in so many practical applications. …”
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Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
Published 2018“…The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. …”
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Biometric identification and recognition for iris using failure rejection rate (FRR) / Musab A. M. Ali
Published 2016“…Hence, the objectives of this research are new algorithms development significantly for iris segmentation specifically the proposed Fusion of Profile and Mask Technique (FPM) specifically in getting the actual center of the pupil with high level of accuracy prior to iris localization task, followed by a particular enhancement in iris normalization that is the application of quarter size of an iris image (instead of processing a whole or half size of an iris image) and for better precision and faster recognition with the robust Support Vector Machine (SVM) as classifier. …”
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