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 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 Biometric Iris Recognition of Person from an Image at Long Distance using Chronological Monarch Butterfly Optimization based Deep Belief Network

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1975-1983
Keyword(s):  
Feature Extraction ◽  
Hough Transform ◽  
Iris Recognition ◽  
Median Filter ◽  
Monarch Butterfly ◽  
Deep Belief Network ◽  
Long Distance ◽  
Belief Network ◽  
Monarch Butterfly Optimization

Now days, for the identification of personal information of a person, biometrics is mostly used. Also for the personal identification, the recognition of eye based biometric feature extraction is the most powerful tool. The biometric is an important identity to identify the individual. But in real time it is quite difficult to capture the better quality of iris images. The images obtained are more degraded due to the lack of texture, blur. In this paper, more convenient method is presented for extracting the features of biometric images. The method Iris Recognition at-a Distance (IAAD) is used to extract the iris features of biometric image and to enhance the quality of an image in a biometric system. The Chronological Monarch Butterfly Optimization -based Deep Belief Network (Chronological MBO-based DBN) is proposed for iris recognition to get better accuracy. The Monarch Butterfly Optimization algorithm is used to arrange the Chronological assumption of an iris image. Also, the Hough Transform algorithm is used for detection of iris circle and edge. The scaT T loop descriptor and the Local Gradient Pattern (LGP) are used for feature extraction, which is fed to the Chronological MBO-based DBN for iris recognition that enhances the accuracy. The Daugman’s rubber sheet model, median filter and trained neural network are used for normalization and segmentation. The UBIRIS.v1 database is used to take an iris recognition images and MATLAB is used for programming of for reading the iris images and for performing the Hough transform operations. The iris recognition at a distance 4 to 8 meter is done with the help of simulation result. The performance is analyzed based on the metrics, like False Acceptance Rate (FAR), accuracy, and False Rejection Rate (FRR) with the value of 0.4847%, 96.078%, and 0.4745%

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 Noncircular Iris Segmentation Based on Weighted Adaptive Hough Transform Using Smartphone Database

2018 ◽  
Vol 15 (2) ◽  
pp. 739-743 ◽  
Author(s):  
Noor Amjed ◽  
Fatimah Khalid ◽  
Rahmita Wirza O. K. Rahmat ◽  
Hizmawati Binit Madzin
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Recognition System ◽  
Iris Segmentation ◽  
Negative Image ◽  
Processing Step ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
High Level ◽  
Imaging Conditions

Iris segmentation methods work based on ideal imaging conditions which produce good output results. However, the segmentation accuracy of an iris recognition system significantly influences its performance, especially with data that captured in unconstrained environment of the Smartphone. This paper proposes a novel segmentation method for unconstrained environment of the Smartphone videos based on choose the best frames from the videos and try to enhance the contrast of this frames by applying the two fuzzy logic membership functions on the negative image which delimit between dark and bright regions in able to make the dark region darker and the bright region brighter. This pre-processing step Facilitates the work of the Weighted Adaptive Hough Transform to automatically find the diameter of the iris region to apply the osiris v4.1. The proposed method results on the video of (Mobile Iris Challenge Evaluation (MICHE))-I, iris databases indicate a high level of accuracy and more efficient computationally using the proposed technique.

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%.

scholarly journals Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Ying Chen ◽  
Yuanning Liu ◽  
Xiaodong Zhu ◽  
Huiling Chen ◽  
Fei He ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Quality Evaluation ◽  
Feature Fusion ◽  
Recognition System ◽  
Experimental Results ◽  
Iris Image ◽  
Local Quality ◽  
Evaluation Algorithm ◽  
Evaluation Parameters

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks’ information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples’ own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

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>

An Efficient and Automatic Iris Recognition System Using ICM Neural Network

2010 ◽  
pp. 445-460
Author(s):  
Guangzhu Xu ◽  
Yide Ma ◽  
Zaifeng Zhang
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Iris Recognition ◽  
Hamming Distance ◽  
Image Database ◽  
Recognition System ◽  
Rectangular Region ◽  
Cortical Model ◽  
Image Preprocessing ◽  
Iris Image

Iris recognition has been shown to be very accurate for human identification. In this chapter, an efficient and automatic iris recognition system using Intersecting Cortical Model (ICM) neural network is presented which includes two parts mainly. The first part is image preprocessing which has three steps. First, iris location is implemented based on local areas. Then the localized iris area is normalized into a rectangular region with a fixed size. At last the iris image enhancement is implemented. In the second part, the ICM neural network is used to generate iris codes and the Hamming Distance between two iris codes is calculated to measure the dissimilarity. In order to evaluate the performance of the proposed algorithm, CASIA v1.0 iris image database is used and the recognition results show that the system has good performance.

ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance

2021 ◽  
Vol 66 (2) ◽  
pp. 167-180
Author(s):  
Swati D. Shirke ◽  
Cherukuri Rajabhushnam
Keyword(s):  
Iris Recognition ◽  
Automatic Segmentation ◽  
Rejection Rate ◽  
Monarch Butterfly ◽  
False Acceptance Rate ◽  
Rubber Sheet ◽  
Monarch Butterfly Optimization ◽  
Scattering Transform ◽  
Tetrolet Transform ◽  
Visual Imaging

Abstract Iris Recognition at-a Distance (IAAD) is a major challenge for researchers due to the defects associated with the visual imaging and poor image quality in dynamic environments, which imposed bad impacts on the accuracy of recognition. Thus, in order to enable the effective IAAD, this paper proposes a new method, named, Chronological Monarch Butterfly Optimization (Chronological MBO)-enabled Neural Network (NN). The recognition of iris using NN is trained with the proposed Chronological MBO, which is developed through the combination of Chronological theory in Monarch Butterfly Optimization (MBO). The recognition becomes effective with the automatic segmentation and the normalization of iris image on the basis of Hough Transform (HT) and Daugman’s rubber sheet model followed with the process of feature extraction with the developed ScatT-LOOP descriptor, which is the integration of scattering transform (ST), Local Optimal Oriented Pattern (LOOP) descriptor, and Tetrolet transform (TT). The developed ScatT-LOOP descriptor extracts the texture as well as the orientation details of image for effective recognition. The analysis is evaluated with the CASIA Iris dataset with respect to the evaluation metrics, accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). The proposed method has the accuracy, FRR, and FAR of 0.97, 0.005, and 0.005, respectively. The experimental results proved that the proposed method is effective than the existing methods of iris recognition.

scholarly journals Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform

2021 ◽  
Vol 11 (5) ◽  
pp. 4037
Author(s):  
Isam Abu Qasmieh ◽  
Hiam Alquran ◽  
Ali Mohammad Alqudah
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Hamming Distance ◽  
Specular Reflection ◽  
Recognition System ◽  
Reference Image ◽  
Support Vector ◽  
Randomized Hough Transform ◽  
Artificial Intelligence Models ◽  
Matching Techniques

A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability.

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