Comparative Analysis of Iris Segmentation and Iris Feature Extraction Techniques

Iris recognition is identification and verification of an individual based on their respective unique iris patterns. This system is preferred because it is stable: Iris of an individual does not change by the passing of time; Unique: Each person has a different Iris pattern; Flexible: it can easily be incorporated into security systems; Reliable: No theft because people can�t create an iris of another person;In our survey project the processes of Iris segmentation and Feature Extraction have been studied in depth.In this survey paper the various techniques that are used in Iris segmentation and Feature extraction processes are compared and analysed and a conclusion is drawn from them

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

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

A Survey on IRIS Recognition System: Comparative Study

Because of an increasing emphasis on security, Iris recognition has gained a great attention in both research and practical applications over the past decade. The demand for iris recognition in the various fields of access control reducing fraudulent transactions in electronic commences, security at border areas etc is increasing day by day due to its high accuracy, reliability and uniqueness. A review of various segmentation approaches used in iris recognition is done in this paper. The performance of the iris recognition systems depends heavily on segmentation and normalization techniques

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System

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 Integro-Differential 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

Non-cooperative iris recognition

The dramatic growth in practical applications for iris biometrics has been accompanied by relevant developments in the underlying algorithms and techniques. Along with the research focused on near-infrared images captured with subject cooperation, e orts are being made to minimize the trade-o between the quality of the captured data and the recognition accuracy on less constrained environments, where images are obtained at the visible wavelength, at increased distances, over simpli ed acquisition protocols and adverse lightning conditions. At a rst stage, interpolation e ects on normalization process are addressed, pointing the outcomes in the overall recognition error rates. Secondly, a couple of post-processing steps to the Daugman's approach are performed, attempting to increase its performance in the particular unconstrained environments this thesis assumes. Analysis on both frequency and spatial domains and nally pattern recognition methods are applied in such e orts. This thesis embodies the study on how subject recognition can be achieved, without his cooperation, making use of iris data captured at-a-distance, on-the-move and at visible wavelength conditions. Widely used methods designed for constrained scenarios are analyzed.Fundação para a Ciência e a Tecnologia (FCT

Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition

An increasing need for biometrics recognition systems has grown substantially to address the issues of recognition and identification, especially in highly dense areas such as airports, train stations, and financial transactions. Evidence of these can be seen in some airports and also the implementation of these technologies in our mobile phones. Among the most popular biometric technologies include facial, fingerprints, and iris recognition. The iris recognition is considered by many researchers to be the most accurate and reliable form of biometric recognition because iris can neither be surgically operated with a chance of losing slight nor change due to aging. However, presently most iris recognition systems available can only recognize iris image with frontal-looking and high-quality images. Angular image and partially capture image cannot be authenticated with the existing method of iris recognition. This research investigates the possibility of developing a technique for recognition partially captured iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%, and 12.5% and to find a threshold for a minimum amount of iris region required to authenticate the individual. The research also developed and implemented two Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre wavelet filter is to enhance the feature extraction technique. Selected iris images from CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced technique. The technique was able to produce recognition accuracy between 70 – 90% CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with 74.95%, and MMU with 94.45%

Multispectral iris recognition analysis: Techniques and evaluation

This thesis explores the benefits of using multispectral iris information acquired using a narrow-band multispectral imaging system. Commercial iris recognition systems typically sense the iridal reflection pertaining to the near-infrared (IR) range of the electromagnetic spectrum. While near-infrared imaging does give a very reasonable image of the iris texture, it only exploits a narrow band of spectral information. By incorporating other wavelength ranges (infrared, red, green, blue) in iris recognition systems, the reflectance and absorbance properties of the iris tissue can be exploited to enhance recognition performance. Furthermore, the impact of eye color on iris matching performance can be determined. In this work, a multispectral iris image acquisition system was assembled in order to procure data from human subjects. Multispectral images pertaining to 70 different eyes (35 subjects) were acquired using this setup. Three different iris localization algorithms were developed in order to isolate the iris information from the acquired images. While the first technique relied on the evidence presented by a single spectral channel (viz., near-infrared), the other two techniques exploited the information represented in multiple channels. Experimental results confirm the benefits of utilizing multiple channel information for iris segmentation. Next, an image enhancement technique using the CIE L*a*b* histogram equalization method was designed to improve the quality of the multispectral images. Further, a novel encoding method based on normalized pixel intensities was developed to represent the segmented iris images. The proposed encoding algorithm, when used in conjunction with the traditional texture-based scheme, was observed to result in very good matching performance. The work also explored the matching interoperability of iris images across multiple channels. This thesis clearly asserts the benefits of multispectral iris processing, and provides a foundation for further research in this topic

Recognition of Nonideal Iris Images Using Shape Guided Approach and Game Theory

Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The main objective of this thesis is to develop a nonideal iris recognition system by using active contour methods, Genetic Algorithms (GAs), shape guided model, Adaptive Asymmetrical Support Vector Machines (AASVMs) and Game Theory (GT). In this thesis, the proposed iris recognition method is divided into two phases: (1) cooperative iris recognition, and (2) noncooperative iris recognition. While most state-of-the-art iris recognition algorithms have focused on the preprocessing of iris images, recently, important new directions have been identified in iris biometrics research. These include optimal feature selection and iris pattern classification. In the first phase, we propose an iris recognition scheme based on GAs and asymmetrical SVMs. Instead of using the whole iris region, we elicit the iris information between the collarette and the pupil boundary to suppress the effects of eyelid and eyelash occlusions and to minimize the matching error. In the second phase, we process the nonideal iris images that are captured in unconstrained situations and those affected by several nonideal factors. The proposed noncooperative iris recognition method is further divided into three approaches. In the first approach of the second phase, we apply active contour-based curve evolution approaches to segment the inner/outer boundaries accurately from the nonideal iris images. The proposed active contour-based approaches show a reasonable performance when the iris/sclera boundary is separated by a blurred boundary. In the second approach, we describe a new iris segmentation scheme using GT to elicit iris/pupil boundary from a nonideal iris image. We apply a parallel game-theoretic decision making procedure by modifying Chakraborty and Duncan's algorithm to form a unified approach, which is robust to noise and poor localization and less affected by weak iris/sclera boundary. Finally, to further improve the segmentation performance, we propose a variational model to localize the iris region belonging to the given shape space using active contour method, a geometric shape prior and the Mumford-Shah functional. The verification and identification performance of the proposed scheme is validated using four challenging nonideal iris datasets, namely, the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and the WVU Nonideal, plus the non-homogeneous combined dataset. We have conducted several sets of experiments and finally, the proposed approach has achieved a Genuine Accept Rate (GAR) of 97.34% on the combined dataset at the fixed False Accept Rate (FAR) of 0.001% with an Equal Error Rate (EER) of 0.81%. The highest Correct Recognition Rate (CRR) obtained by the proposed iris recognition system is 97.39%

Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System

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