Iris Recognition Using Local and Global Iris Image Moment Features

2019 ◽  
Author(s):  
Pradeep Patil ◽  
K. Vasanth
Keyword(s):  
Iris Recognition ◽  
Iris Image ◽  
Image Moment ◽  
Moment Features

IMPROVEMENT OF IRIS RECOGNITION PERFORMANCE USING REGION-BASED ACTIVE CONTOURS, GENETIC ALGORITHMS AND SVMs

2010 ◽  
Vol 24 (08) ◽  
pp. 1209-1236 ◽  
Author(s):  
KAUSHIK ROY ◽  
PRABIR BHATTACHARYA
Keyword(s):  
Genetic Algorithms ◽  
Iris Recognition ◽  
Active Contours ◽  
Active Contour Model ◽  
Recognition Performance ◽  
Texture Features ◽  
Motion Blur ◽  
Misclassification Error ◽  
Support Vector ◽  
Iris Image

Most existing iris recognition algorithms focus on the processing and recognition of the ideal iris images that are acquired in a controlled environment. In this paper, we process the nonideal iris images that are captured in an unconstrained situation and are affected severely by gaze deviation, eyelids and eyelashes occlusions, nonuniform intensity, motion blur, reflections, etc. The proposed iris recognition algorithm has three novelties as compared to the previous works; firstly, we deploy a region-based active contour model to segment a nonideal iris image with intensity inhomogeneity; secondly, genetic algorithms (GAs) are deployed to select the subset of informative texture features without compromising the recognition accuracy; Thirdly, to speed up the matching process and to control the misclassification error, we apply a combined approach called the adaptive asymmetrical support vector machines (AASVMs). The verification and identification performance of the proposed scheme is validated on three challenging iris image datasets, namely, the ICE 2005, the WVU Nonideal, and the UBIRIS Version 1.

scholarly journals A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2790
Author(s):  
Qi Xiong ◽  
Xinman Zhang ◽  
Shaobo He ◽  
Jun Shen
Keyword(s):  
Fractional Order ◽  
Iris Recognition ◽  
Search Algorithm ◽  
Fitness Function ◽  
Histogram Equalization ◽  
Enhancement Effect ◽  
Local Optimum ◽  
Long Distance ◽  
Iris Image ◽  
Bee Colony

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

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.

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