scholarly journals Eyelids, eyelashes detection algorithm and houghtransform method for noise removal in iris recognition

2020 ◽  
Vol 18 (2) ◽  
pp. 731
Author(s):  
Bounegta Nadia ◽  
Bassou Abdessalam ◽  
Beladgham Mohamed
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Iris Recognition ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Detection Technique ◽  
Biometric System ◽  
Transform Method ◽  
Iris Image ◽  
Identical Feature

<p><span>The biometric system is based on human’s behavioral and physical characteristics. Among all of these, iris has unique structure, higher accuracy and it can remain stable over a person’s life. Iris recognition is the method by which system recognize a person by their unique identical feature found in the iris. Iris recognition technology includes four subsections as, capturing of the iris image, segmentation, extraction of the needed features and matching. This paper is a detail description of eyelids; eyelashes detection technique and Hough transform method applied on iris image. </span></p>

Iris Boundary Localization Based on Improve Hough Transform

2013 ◽  
Vol 760-762 ◽  
pp. 1576-1580
Author(s):  
Guo Yu Zhang ◽  
Hui Zhao ◽  
Min Han ◽  
Li Ling Chen
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Traditional Method ◽  
Iris Recognition ◽  
Recognition System ◽  
Low Contrast ◽  
Exterior Boundary ◽  
Iris Image ◽  
Interior Boundary ◽  
Key Steps

ris location is one of the key steps of iris recognition system. Non-ideal iris image has some problems, such as eyelid and eyelash occlusion, low contrast of iris and sclera, uneven illumination, and so on. Because of that, its difficult to identify the boundary, especially the exterior boundary. Therefore, this paper proposes a method based on the improved Hough Transform. First, use the minimum method to find the datum point in the pupil, after that identify the valid area of the interior boundary base on that point. Apply the improved Hough Transform to that valid area to identify the interior boundary of the iris image. Then regard the center of the interior circle as our new datum point, use the same method to identify the exterior boundary. Experiment results show that our algorithm has higher accuracy than traditional method on the non-ideal iris image segmentation.

scholarly journals An Improved Bovine Iris Segmentation Method

2019 ◽  
Vol 267 ◽  
pp. 03002
Author(s):  
Zhongliang Luo ◽  
Jingguo Dai ◽  
Yingbiao Jia ◽  
Jiazhong He
Keyword(s):  
Image Segmentation ◽  
Iris Recognition ◽  
Noise Suppression ◽  
Safety Management ◽  
Iris Segmentation ◽  
Iris Image ◽  
Enhancement Method ◽  
Iris Feature Extraction ◽  
Speed And Accuracy ◽  
Image Segmentation Algorithm

In order to improve the performance of bovine iris image segmentation, an improved iris image segmentation algorithm is proposed according to the characteristics of bovine iris image. Firstly, based on mathematical morphology and noise suppression template, the inner and outer edges of bovine iris are detected by dynamic contour tracking and least squares fitting ellipse respectively. Then, the annular iris region is normalized. Finally, the normalized iris image is enhanced with adaptive image enhancement method. The experimental results show that the algorithm can effectively segment iris region, it has good performance of speed and accuracy for iris segmentation, and can eliminate the effects of uneven illumination, iris shrinkage and rotation, it promotes iris feature extraction and matching, which has certain reference significance for iris recognition research and meat food safety management of large livestock.

scholarly journals A Novel Iris Segmentation Scheme

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Chen-Chung Liu ◽  
Pei-Chung Chung ◽  
Chia-Ming Lyu ◽  
Jui Liu ◽  
Shyr-Shen Yu
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Image Database ◽  
Recognition System ◽  
Experimental Results ◽  
Circle Fitting ◽  
Iris Segmentation ◽  
Iris Image ◽  
Conventional Methods ◽  
Key Steps

One of the key steps in the iris recognition system is the accurate iris segmentation from its surrounding noises including pupil, sclera, eyelashes, and eyebrows of a captured eye-image. This paper presents a novel iris segmentation scheme which utilizes the orientation matching transform to outline the outer and inner iris boundaries initially. It then employs Delogne-Kåsa circle fitting (instead of the traditional Hough transform) to further eliminate the outlier points to extract a more precise iris area from an eye-image. In the extracted iris region, the proposed scheme further utilizes the differences in the intensity and positional characteristics of the iris, eyelid, and eyelashes to detect and delete these noises. The scheme is then applied on iris image database, UBIRIS.v1. The experimental results show that the presented scheme provides a more effective and efficient iris segmentation than other conventional methods.

scholarly journals Iris Image Segmentation and Localization using Dynamic Reconfigurable Processor (DRP)

2020 ◽  
Vol 2 (3) ◽  
pp. 147-155
Author(s):  
Smaran S. Rao ◽  
Shreyas R. ◽  
Gajanan Maske ◽  
Antara Roy Choudhury
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Iris Recognition ◽  
Image Capture ◽  
Iris Localization ◽  
Reconfigurable Processor ◽  
Iris Image ◽  
Survey Paper ◽  
Configurable Processor ◽  
Iris Feature Extraction

Recognition of the Iris is among the finest techniques in the field of bio-metrics identification, because the iris has characteristics that are unique and stay the same all through the individual’s life. Iris recognition phases are namely image acquisition, segmentation of iris, localization of iris, feature extraction of iris and matching. This paper, which is an extension of the survey paper Smaran et.al[1], concentrates purely on the procedures of image capture, segmentation as well as localization of the iris. The aim of the paper is to optimize the above mentioned processes in terms of distance of capturing the image, time taken for memory and computation requirements, using the DRP (Dynamic Re-Configurable Processor) technology, uniquely developed by Renesas Electronics (www.renesas.com).

scholarly journals Iris Recognition at-a-Distance by Means of Chronological MBO-Based DBN

2019 ◽  
Vol 8 (12) ◽  
pp. 4540-4552
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Monarch Butterfly ◽  
Recognition System ◽  
Monarch Butterfly Optimization ◽  
Iris Image ◽  
Safety Reason ◽  
Local Gradient ◽  
Detection Schemes ◽  
Actual Method

Now a days, Iris recognition is wieldy used for the identification of person. The superior bit of 1 countries exploits biometric system for safety reason with the conclusion goal that in runway boarding, custom freedom, gathering passage, etc. The Iris detection at-a-Distance (IAAD) framework is generally used to identify the person in most of the applications. In this system, different features of iris image are extracted in addition enhances the superiority of iris image. Over the span of the most recent ages there consume raised various structures to design and finish iris affirmation systems which works at longer separation going from one meter to sixty meter. Because of such long scope of iris detection schemes in addition iris attainment scheme provides for the best applications to the client. Therefore, It is necessary to design an effective algorithm for IAAD is necessary. In this article, an actual method for iris recognition is presents. A Chronological Monarch Butterfly Optimization -based Deep Belief Network (Chronological MBO-based DBN) technique is anticipated for iris detection.This technique algorithm is the combination of Chronological theory with the Monarch Butterfly Optimization. It is utilized to mastermind the sequential presumption of an iris picture. Additionally, the Hough Transform calculation is utilized for discovery of iris circle and edge. To enhance the accuracy of anticipated iris recognition system ScatT-Loop descriptor and the Local Gradient Pattern (LGP) are fed to the Chronological MBO-based DBN algorithm and these are castoff to abstract the dissimilar features of an iris picture. The dataset used for these tactices are UBIRIS.v1 For the normalization and segmentation of an iris image is done by by means of Dougman's rubber sheet model. This system is established on MATLAB for executing the Hough transform procedures also for reading the iris images. The simulation results shows that this system successfully recognize the iris at a distance 4 to 8 meter. Different performance parameters like as FAR accuracy, too FRR shows better results in this anticipated work.

scholarly journals Soft Lens Detection in Iris Image using Lens Boundary Analysis and Pattern Recognition Approach

2021 ◽  
Vol 10 (1) ◽  
pp. 241-250
Keyword(s):  
Pattern Recognition ◽  
Iris Recognition ◽  
Recognition System ◽  
Detection Algorithm ◽  
Iris Image ◽  
Ridge Detection ◽  
Visibility Condition ◽  
Pattern Recognition Approach ◽  
Contact Lens Detection ◽  
False Reject Rate

Recent studies have demonstrated that the soft lens wearing during iris recognition has indicated the increase of false reject rate. It denies the strong belief that the soft lens wearing will cause no performance degradation. Therefore, it is a necessity for an iris recognition system to be able to detect the presence of soft lens prior to iris recognition. As a first step towards soft lens detection, this study proposed a method for segmenting the soft lens boundary in iris images. However, segmenting the soft lens boundary is a very challenging task due to its marginal contrast. Besides, the flash lighting effect during the iris image enrolment has caused the image to suffer from inconsistent illumination. In addition, the visibility condition of the soft lens boundary may be discerned as a bright or dark ridge as a result of the flash lighting. Three image enhancement techniques were therefore proposed in order to enhance the contrast of the soft lens boundary and to provide an even distribution of intensities across the image. A method called summed-histogram has been incorporated as a solution to classify the visibility condition of the soft lens boundary automatically. The visibility condition of the ridge is used to determine the directional directive magnitude by the ridge detection algorithm. The proposed method was evaluated with Notre Dame Contact Lens Detection 2013 database. Results showed that the proposed method has successfully segment the soft lens boundary with an accuracy of over 92%.

An Improved Randomized Hough Transform Method for the Cotton Recognition

2012 ◽  
Vol 189 ◽  
pp. 383-387
Author(s):  
Kun Liu ◽  
Shu Min Fei ◽  
Mu Lan Wang
Keyword(s):  
Hough Transform ◽  
Mathematic Model ◽  
Detection Technique ◽  
Transform Method ◽  
Model Based ◽  
Novel Approach ◽  
Randomized Hough Transform ◽  
Boundary Trace ◽  
Contour Information ◽  
Harvesting Robot

The cotton recognition would become rather difficult during the application of the cotton harvesting robot (CHR) in the case where the cotton is sheltered or covered by other objects. In this paper, a novel approach of cotton detection based on the improved randomized Hough transform (IRHT) for this case is proposed. Based on the contour information from the boundary trace, the mathematic model-based IRHT is derived based on a modified circular detection technique. It yields a well agreement with the requirements of the precision and rating of CHR.

scholarly journals Iris Segmentation Based on Black Hole Algorithm for Biometric System

2020 ◽  
Vol 10 (2) ◽  
pp. 281-287
Keyword(s):  
Black Hole ◽  
Hough Transform ◽  
Iris Recognition ◽  
Optimization Method ◽  
Circle Fitting ◽  
Iris Segmentation ◽  
Biometric System ◽  
Control Applications ◽  
Segmented Images ◽  
Black Hole Algorithm

Current iris recognition schemes such as IntegroDifferential method, Hough Transform, Watershed Transform Circle Fitting, and Circular Hough Transformation (CHT) are used to find circular parameters between pupil and iris. Segmentation process of an eye image using the circular parameters toextracts the iris region still can be further improved. In this paper, we introduced an optimization method of circular parameters detection for iris segmentation based on Black Hole Algorithm (BHA). The proposed segmentation algorithm utilizes a computational model of the pixels’ value to detect the iris boundary. The BHA searches for center radius of both pupil and iris. The system tests the CASIA Iris Interval V3 database by on MATLAB. The segmented images show an accuracy of 98.3%. In short, the segmentation-based on BHA is efficient to identify the iris for any future access control applications.

An Improved MeanShift Insulator Image Segmentation Algorithm

2013 ◽  
Vol 634-638 ◽  
pp. 3945-3949
Author(s):  
Fang Ting ◽  
Yun Biao Zhao ◽  
Xing Liu Hu ◽  
Xia Bing
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Edge Detection Algorithm ◽  
Mean Shift Algorithm ◽  
Image Segmentation Algorithm

According to color characteristics of insulator ,The paper is based on HSI model and Mean Shift algorithm in order to segmentation insulator images. It firstly introduces theory of mean shift algorithm, then explains morphological processing with edge detection algorithm to extract the insulator images contour. Last using Hough transform to obtain the segmentation results. Experiment indicates that VC6.0 combined with opencv simulation the proposed algorithm could effectively extract segmentation of insulator images provides the basis for follow-up determination of insulator faults.

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