Human Iris Recognition Research Articles

A review of human iris identification and recognition using soft computing techniques

Computer vision and iris recognition are fields that are constantly evolving. During the past ten years, there has been a significant increase in the level of interest in computer vision, Iris recognition, soft computing techniques, neural networks, etc. This paper reviews different research papers on iris recognition. Lastly, it focuses on the international status of iris recognition.

Improvement of iris recognition technology for biometric identification of a person

This topic is very relevant in the field of artificial intelligence as a direction of pattern recognition. In this work, the iris of the eye is considered as an image. Artificial intelligence makes this technology more accessible for use in CCTV cameras, smartphones and various areas of human activity. The article reflects the results of a study of methods and technologies of pattern recognition on the example of the human iris. The aim of the work was to study methods and technologies for human iris recognition and iris recognition of employees of a particular organization using EyeLock equipment by comparing segmentation results with Daugman standard segmentation. Comparison analysis of segmentation results with standard segmentation can be done by directly measuring the number of correctly segmented irises in both methods, or by indirectly measuring the effect of segmentation on iris recognition performance. The method using the Daugman integral-differential operator has the greatest efficiency. The performance of the neural network has been improved. To use a neural network to classify iris profiles, we selected sets of images (images per person) as training images, and the rest of the images were used as test images. Training time (in seconds): for the Daugman method 170.7, and for the parabolic method 204.7. The Daugman integro-differential operator is applied to the captured image to obtain the "maximum integral derivative of the contour" with ever-increasing radius on "successively decreasing scales" in three parameters: center coordinates and radius. Finding the maximum when the search coordinates deviate along an unwinding spiral. Methods and techniques for pattern recognition have been investigated using the human iris

Towards more accurate and efficient human iris recognition model using deep learning technology

In this study, an end-to-end human iris recognition system is presented to automatically identify individuals for high level of security purposes. The deep learning technology based new 2D convolutional neural network (CNN) model is introduced for extracting the features and classifying the iris patterns. Firstly, the iris dataset is collected, preprocessed and augmented. The dataset are expanded and enhanced using data augmentation, histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) techniques. Secondly, the features of the iris patterns were extracted and classified using CNN. The structure of CNN comprises of convolutional layers and ReLu layers for extracting the features, pooling layers for reducing the parameters, fully connected layer and Softmax layer for classifying the extracted features into N classes. For the training process and updating the weights, the backpropagation algorithm and adaptive moment estimation Adam optimizer are used. The experimental results carried out based on a graphics processing unit (GPU) and using Matlab. The overall training accuracy of the introduced system was 95.33% with a consumption time of 17.59 minutes for training set. While the testing accuracy 100% with a consumption time of 12 seconds. The introduced iris recognition system has been successfully applied.

Comparison of Feature Extraction Technique for Segmentation in Human Iris Recognition Under Uncontrolled Environment Using CNN Algorithm with SVM Classifier

The main aim of this study is to compare the radical segmentation of human iris using two different machine learning algorithms in an uncontrolled environment image dataset. Materials and method: Images taken from MMU iris dataset, Convolutional Neural Network (CNN) model, and Support Vector Machine (SVM) model implemented to segment iris in uncontrolled environment image with 50 samples per group. Results: MATLAB simulation result shows that CNN has accuracy of 94% and SVM has 72% in segmenting iris. Attained significant accuracy (0.001) in SPSS statistical analysis. Conclusion: For the given images, proposed CNN shows better accuracy than SVM classifier in iris segmentation tasks.

A survey of iris datasets

Research on human eye image processing and iris recognition has grown steadily over the last few decades. It is important for researchers interested in this discipline to know the relevant datasets in this area to (i) be able to compare their results and (ii) speed up their research using existing datasets rather than creating custom datasets. In this paper, we provide a comprehensive overview of the existing publicly available datasets and their popularity in the research community using a bibliometric approach. We reviewed 158 different iris datasets referenced from the 689 most relevant research articles indexed by the Web of Science online library. We categorized the datasets and described the properties important for performing relevant research. We provide an overview of the databases per category to help investigators conducting research in the domain of iris recognition to identify relevant datasets.

IJMLNCE Editorial Note Volume No 04, Issue No 01

IJMLNCE Editorial Note Volume No 04, Issue No 01

The Performance Enhancement Systems of Human Iris Pattern and Recognition Method through Digital Authentication Application

Human iris and recognition patterns have been recognized as the best biometric marking ever found, owing to the uniqueness of iris and the textured iris patterns tend to remain natural, unchangeable and recognizable through existence. Mathematical analyses of the special stable patterns formed within the iris include Iris detection methods and a comparative analysis is carried out utilizing an established database. In this document, a clean electoral system is created to build a fraud-free ID list of electors. To find the Iris and Eyes, the algorithm of canny edge detection is used, Dougman's normalization procedure is used, object filters are added and finally the corresponding process is conducted for the Euclidian set. Biometric authentication confirms our identification by being a simple and increasingly secure method. We implement a weighted, majority voting process for all biometric authentication systems utilizing a bit wise contrast between inscription and biometric models to resolve this problem and to enable Iris identification in less than ideal images. We also observed that the approach outdoes the current majority and efficient bit sorting strategies through a set of tests with the database CASIA iris.  Our approach is an easy and efficient way to boost the accuracy of established iris detection systems.

Enhancing human iris recognition performance in unconstrained environment using ensemble of convolutional and residual deep neural network models

Despite the prominent advancements in iris recognition, unconstrained image acquisition through heterogeneous sensors has been a major obstacle in applying it for large-scale applications. In recent years, deep convolutional networks have achieved remarkable performance in the field of computer vision and have been employed in iris applications. In this study, three distinct models based on the ensemble of convolutional and residual blocks are proposed to enrich heterogeneous (cross-sensor) iris recognition. In order to analyze their quantitative performances, extensive experiments are carried out on two publicly available iris databases, ND-iris-0405 dataset and ND-CrossSensor-Iris-2013 dataset. Further, the final model has been scrutinized based on the least error rate and then fused using score-level fusion with two preeminent feature extraction methods, i.e., scale-invariant feature transform and binarized statistical information features. The resultant model is examined for cross-sensor iris recognition and reported the top two error rates as 1.01% and 1.12%. It infers that the proposed approach constitutes vital discerning iris features and can recognize that the micro-patterns exist inside the iris region. Furthermore, a comparative study is carried out with the state of the art, where the proposed approach obtains significantly improved performance.

Human Iris Recognition Based on Hybrid Technique

Iris recognition is a biometric technique that uses iris pattern information to detect person identification. Initially, the system find out the boundary of the pupil and iris. Then, Circular Hough transform used to find out the center of both pupil and iris in order to crop iris part from the eye image. After that, Daugman’s Rubber Sheet model utilized for performing the normalizing step. Then, features extracted based on Legendre moment and Local Quantized. Several orders value with many region of iris have been used to get best value, which satisfied the highest recognition rate. Matching was performed by City Block Distance. The simulation was carried out using samples from CASIA.v4-Interval database, the main tool for programming is MATLAB

Iris Recognition After Death

This paper presents a comprehensive study of post-mortem human iris recognition carried out for 1200 near-infrared and 1787 visible-light samples collected from 37 deceased individuals kept in mortuary conditions. We used four independent iris recognition methods (three commercial and one academic) to analyze genuine and impostor comparison scores and check the dynamics of iris quality decay over a period of up to 814 h after death. This study shows that post-mortem iris recognition may be close-to-perfect approximately 5–7 h after death and occasionally is still viable even 21 days after death. These conclusions contradict the statements present in the past literature that the iris is unusable as a biometrics shortly after death, and show that the dynamics of post-mortem changes to the iris that are important for biometric identification are more moderate than previously hypothesized. This paper contains a thorough medical commentary that helps to understand which post-mortem metamorphoses of the eye may impact the performance of automatic iris recognition. An important finding is that false-match probability is higher when live iris images are compared with post-mortem samples than when only live samples are used in comparisons. This paper conforms to reproducible research and the database used in this study is made publicly available to facilitate research on post-mortem iris recognition. To the best of our knowledge, this paper offers the most comprehensive evaluation of post-mortem iris recognition and the largest database of post-mortem iris images.

Hybrid robust iris recognition approach using iris image pre‐processing, two‐dimensional gabor features and multi‐layer perceptron neural network/PSO

Computational intelligence is employed to solve factual and complicated global problems, though neural networks (NNs) and evolutionary computing have also affected these issues. Biometric traits are applicable for detecting crime in security systems because they offer attractive features such as stability and uniqueness. Although various methods have been proposed for this objective, feature shortcomings such as computational complexity, long run times, and high memory consumption remain. The current study proposes a novel human iris recognition approach based on a multi-layer perceptron NN and particle swarm optimisation (PSO) algorithms to train the network in order to increase generalisation performance. A combination of these algorithms was used as a classifier. A pre-processing step was performed on the iris images to improve the results and two-dimensional gabor kernel feature extraction was applied. The data was normalised, trained, and tested using the proposed method. A PSO algorithm was applied to train the NN for data classification. The experimental results show that the proposed method performs better than many other well-known techniques. The benchmark Chinese Academy of Science and Institute of Automation (CASIA)-iris V3 and Center for Machine Learning and Intelligent Systems at the University of California, Irvine (UCI) machine learning repository datasets were used for testing and comparison.

Enhanced Human Iris Recognition System based on Procedure of Authentication System

Enhanced Human Iris Recognition System based on Procedure of Authentication System

Human Iris Recognition for Clean Electoral Process in India by Creating a Fraud Free Voter Registration List

Human Iris pattern matching and recognition system is considered to be the best biometric identification found so far because of the unique features found in the iris and moreover the textured patterns of iris remain stable, invariant and distinct throughout the whole life. Iris recognition techniques involve a mathematical analysis of the unique stable patterns that are structured within the iris and then the comparison is being done with an already existing database. In this paper the implementation of creating a fraud free voter ID list is being done as to make a clean Electoral environment. For this localization of Iris and Pupils are done by canny edge detection algorithm, Normalization is done by Dougman's Normalization method and feature extraction is being done using Log Gabor Filter and lastly method of matching is accomplished by Euclidian distance1,2. MATLAB 2011 version is used for developing the present study, and much of the emphasis is given on software for Recognition of Irises in an efficient manner.

A Powerful Yet Efficient Iris Recognition Based on Local Binary Quantization

A secure identification system based on human iris recognition has been an attractive goal for researchers for a long time. In this paper, we propose an efficient iris recognition scheme. To significantly reduce the computational time that is required for locating the iris boundaries in a captured eye image, only the 3- and 9- o’clock sides of the iris are considered in our scheme as a search area for finding the outer boundary of the iris. The donut-shape of the iris is then mapped into a rectangular region with constant dimensions to mitigate the problem of changing the iris size that may occur due to having variations in the image capturing conditions. Thenceforth, the lower half of the iris ring is considered as a region of interest for feature extraction. This region is slightly interfered by eyelids and eyelashes. The feature extraction process is performed by a proposed local binary quantization technique. This technique requires a lower computational time than that of other techniques that extract the features of an iris using wavelet analysis (i.e., such as Fourier and Gabor analysis). It should be borne in mind that the computational time of wavelet analysis could be a challenge in online applications. Furthermore, the parameters of wavelet analysis are set in advance and hence they may not adapt with some noises and changes in the iris image as a result of having variations in the image capturing conditions. However, the proposed local binary quantization technique is not affected by these variations. The CASIA iris database is used to conduct several experiments that show the performance of the proposed scheme. Compared to other schemes, the proposed scheme achieves a correct segmentation rate of 99% at a low segmentation time and a correct recognition rate of 99.34%. DOI: http://dx.doi.org/10.5755/j01.itc.43.3.5225

Circular Construction of Iris Feature Vectors for Human Iris Recognition Systems

In this paper, the authors propose a new method to construct feature vectors of human iris based on circular technique. Since an iris has a circular shape, the circular construction of the iris feature vector is expected to have higher ability to capture iris characteristics. Different from the conventional method which constructs iris feature vectors through left to right scanning process, the circular technique scans the pixel-intensity value of iris circularly. As the result, the length of the constructed feature vectors is dependent on the length of iris radius. In the experiments, various recognition methods of feature vectors were compared. The iris recognition results show that the proposed system with Statistical Euclidean Distance method gives the highest recognition accuracy with 84.5%.

Hybrid System of Cellular Automata, PCA and Support Vector Machine for Noise Reduction and Classification in Human Iris Recognition

Hybrid System of Cellular Automata, PCA and Support Vector Machine for Noise Reduction and Classification in Human Iris Recognition

Dual iris matching for biometric identification

In this paper, a dual iris-based biometric identification system that increases the accuracy and the performance of a typical human iris recognition system is proposed. This system detects, isolates, and extracts the iris region from the eye images and maps the circular regions into rectangular polar coordinates according to the preset radial and angular resolutions. The phase responses, obtained from convolving the polar images with 1D log Gabor filter, are quantized to generate the binary iris templates which are compared using the hamming distance. Unlike the conventional method which examines a single eye, the proposed method takes images from both eyes simultaneously. Using it on CASIA Iris database V3, false positive rate and false negative rate were found to be 0 and 9.96 %, respectively, while the overall accuracy was 99.92 %.

An Evaluation of Image Sampling and Compression for Human Iris Recognition

The resilience of identity verification systems to subsampling and compression of human iris images is investigated for three high-performance iris-matching algorithms. For evaluation, 2156 images from 308 irises from the extended Chinese Academy of Sciences Institute of Automation database were mapped into a rectangular format with 512 pixels circumferentially and 80 radially. For identity verification, the 48 rows that were closest to the pupil were taken and images were resized by subsampling their Fourier coefficients. Negligible degradation in verification is observed if at least 171 circumferential and 16 radial Fourier coefficients are preserved, which would correspond to sampling the polar image at 342 times 32 pixels. With JPEG2000 compression, improved matching performance is observed down to 0.3 b/pixel (bpp), attributed to noise reduction without a significant loss of texture. To ensure that the iris-matching algorithms studied are not degraded by image compression, it is recommended that normalized iris images should be exchanged at 512 times 80 pixel resolution, compressed by JPEG 2000 to 0.5 bpp. This achieves a smaller file size than the ANSI/INCITS 379-2004 iris image interchange format.

Human Iris Recognition using Wavelet Transform and Neural Network

Recently, many researchers have been interested in biometric systems such as fingerprint, handwriting, key-stroke patterns and human iris. From the viewpoint of reliability and robustness, iris recognition is the most attractive biometric system. Moreover, the iris recognition system is a comfortable biometric system, since the video image of an eye can be taken at a distance. In this paper, we discuss human iris recognition, which is based on accurate iris localization, robust feature extraction, and Neural Network classification. The iris region is accurately localized in the eye image using a multiresolution active snake model. For the feature representation, the localized iris image is decomposed using wavelet transform based on dyadic Haar wavelet. Experimental results show the usefulness of wavelet transform in comparison to conventional Gabor transform. In addition, we present a new method for setting initial weight vectors in competitive learning. The proposed initialization method yields better accuracy than the conventional method.

위치 이동에 무관한 홍채 인식을 위한 웨이블렛 변환 기술

본 논문에서 제안한 위치 이동에 무관한 웨이블렛 변환을 이용한 홍채 인식 방법은 영상 획득 장비에 의해 획득한 사용자의 눈 영상에 대하여 홍채 영역만을 추출하기 위한 전처리를 수행하고 전처리를 거친 홍채 영상에 의하여 사용자의 신원을 식별하는데 있어서 홍채 영상의 기울어짐 및 이동 문제를 해결하였다. 이를 위해서 일반적인 웨이블렛을 사용하는 대신, 위치 이동에 무관한 웨이블렛 변환을 통하여 최적의 특징값을 추출한후, 이를 코드화하여 저장한 후, 비교하여 본인 여부를 식별하였다. 실험결과 제안된 방법으로 생성된 특징 벡터와 기존에 등록된 특징 벡터의 일치도 측정에 있어서 종래의 웨이블렛 변환 홍채 인식 방법보다 오인식률(FAR) 및 오거부율(FRR)이 현저하게 감소하였다. This paper proposes the use of a wavelet based image transform algorithm in human iris recognition method and the effectiveness of this technique will be determined in preprocessing of extracting Iris image from the user´s eye obtained by imaging device such as CCD Camera or due to torsional rotation of the eye, and it also resolves the problem caused by invariant under translations and dilations due to tilt of the head. This technique values through the proposed translation-invariant wavelet transform algorithm rather than the conventional wavelet transform method. Therefore we extracted the best-matching iris feature values and compared the stored feature codes with the incoming data to identify the user. As result of our experimentation, this technique demonstrate the significant advantage over verification when it compares with other general types of wavelet algorithm in the measure of FAR & FRR.