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1
Deepa, Dr S. T., and Praneetha V. "Iris Pattern Recognition." IOSR Journal of Computer Engineering 18, no. 04 (2016): 43–50. http://dx.doi.org/10.9790/0661-1804024350.
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Jamaludin, Shahrizan, Nasharuddin Zainal, and W. Mimi Diyana W Zaki. "Deblurring of noisy iris images in iris recognition." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 156–59. http://dx.doi.org/10.11591/eei.v10i1.2467.
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Iris recognition used the iris features to verify and identify the identity of human. The iris has many advantages such as stability over time, easy to use and high recognition accuracy. However, the poor quality of iris images can degrade the recognition accuracy of iris recognition system. The recognition accuracy of this system is depended on the iris pattern quality captured during the iris acquisition. The iris pattern quality can degrade due to the blurry image. Blurry image happened due to the movement during image acquisition and poor camera resolution. Due to that, a deblurring method based on the Wiener filter was proposed to improve the quality of iris pattern. This work is significant since the proposed method can enhance the quality of iris pattern in the blurry image. Based to the results, the proposed method improved the quality of iris pattern in the blurry image. Moreover, it recorded the fastest execution time to improve the quality of iris pattern compared to the other methods.
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Shahrizan, Jamaludin, Zainal Nasharuddin, and Mimi Diyana W. Zaki W. "Deblurring of noisy iris images in iris recognition." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 156–59. https://doi.org/10.11591/eei.v10i1.2467.
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Iris recognition used the iris features to verify and identify the identity of human. The iris has many advantages such as stability over time, easy to use and high recognition accuracy. However, the poor quality of iris images can degrade the recognition accuracy of iris recognition system. The recognition accuracy of this system is depended on the iris pattern quality captured during the iris acquisition. The iris pattern quality can degrade due to the blurry image. Blurry image happened due to the movement during image acquisition and poor camera resolution. Due to that, a deblurring method based on the Wiener filter was proposed to improve the quality of iris pattern. This work is significant since the proposed method can enhance the quality of iris pattern in the blurry image. Based to the results, the proposed method improved the quality of iris pattern in the blurry image. Moreover, it recorded the fastest execution time to improve the quality of iris pattern compared to the other methods.
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Al Rivan, Muhammad Ezar, and Siska Devella. "PENGENALAN IRIS MENGGUNAKAN FITUR LOCAL BINARY PATTERN DAN RBF CLASSIFIER." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 11, no. 1 (2020): 97–106. http://dx.doi.org/10.24176/simet.v11i1.3717.
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Iris merupakan bagian dari mata yang memiliki keunikan. Keunikan pada iris ini menjadi alasan iris digunakan sebagai identitas seperti sidik jari,dan suara. Dibandingkan dengan sidik jari, iris memiliki kelebihan karena letak iris yang lebih terlindungi. Setiap individu memiliki pola iris yang berbeda dan pembentukan pola iris tidak berhubungan dengan faktor genetik individu, sehingga iris merupakan biometrik yang memiliki keunikan yang tinggi dan sulitnya untuk dilakukan pemalsuan biometrik. Identifikasi atau pengenalan iris dilakukan dengan menggunakan citra iris. Pada penelitian ini citra iris akan dilakukan tahap praproses yaitu dengan menghilangkan noise seperti bulu dan kelopak mata, yang kemudian hasil praproses citra iris dilakukan ekstraksi fitur menggunakan algoritma Local Binary Pattern (LBP). Setelah proses ekstraksi fitur dilakukan, proses selanjutnya adalah melakukan pelatihan menggunakan Radial Basis Function (RBF). Setelah proses pelatihan, model RBF diuji dengan data iris yang berbeda. Akurasi tertinggi yang dicapai pada pengenalan iris menggunakan fitur LBP dan RBF yaitu 83,33%.
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Ashraf, Irum. "Enhancing Atm Security System by Using Iris (Eye) Recognition." American Journal of Geospatial Technology 3, no. 1 (2024): 69–75. http://dx.doi.org/10.54536/ajgt.v3i1.2967.
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Newly invented Iris recognition which is a part of biometric identification,offering and purposing an antique method for personal identification, authentication and security by analyzing the random pattern of the iris. By using iris recognition system recognizes the identification of a person from a captured image by comparing it to the human iris patterns stored in an iris template database. The iris template database has been carried out by using three steps the first step is segmentation. Hough transform is used to segment the iris region from the eye image of the CASIA database. The noise and blurring due to eyelid occlusions, reflections is eliminated in the segmentation stage. The third step is normalization. A technique based on Hough Transform was employed on the iris for creating a dimensionally steady and compatible representation of the iris region. The last step and fourth step is feature extraction. In this Local Binary Pattern and Gray level Cooccurrence Matrix are used to extract the features. At last template of the new eye image captured will be compared with the iris template database using Probabilistic Neural Network.
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Hovakimyan, Anna Sedrak, Siranush Gegham Sargsyan, and Arshak Nazaryan. "Self-Organizing Map Application for Iris Recognition." Journal of Communications and Computer Engineering 3, no. 2 (2014): 10. http://dx.doi.org/10.20454/jcce.2013.760.
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Human iris is a good subject of biometrical identification, since iris patterns are unique like fingerprints. Iris is well protected against damage, unlike fingerprints, which can be harder to recognize after years of certain types of manual labor.A problem of iris recognition is considered in the paper. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern classification is an example of pattern recognition: it attempts to assign each input value to one of a given set of classes. Nowadays various techniques are used for this purpose, and in particular artificial neural networks.For iris recognition problem solving Kohenen Self Organizing Maps are suggested to use. The software for iris recognition is developed which is customizable and allows to select the appropriate parameters of the neural network to obtain the most satisfactory results. The developed Self-Organizing Map Library of classes can be used for various kinds of object classification problem solving as well as for any problems suitable to solve with Self-Organizing Maps.
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Leahy, Marion, and Mary Laing. "P060 An analysis of eye colour and iris pattern as a risk factor for skin cancer in immunosuppressed renal transplant recipients." British Journal of Dermatology 191, Supplement_1 (2024): i42. http://dx.doi.org/10.1093/bjd/ljae090.087.
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Abstract Renal transplant recipients (RTRs) are at increased risk of keratinocyte skin cancers, with a tendency to have multiple, aggressive and difficult-to-treat tumours. The eye and the skin share the same embryological ectoderm. Iris pattern has recently been reported as a predictive risk factor for skin cancer in nonimmunosuppressed Southern European and Irish populations. Our aim was to analyse whether an individual’s iris pattern is an independent risk factor for the development of keratinocyte carcinoma in RTRs. Iris patterns of 110 RTRs were evaluated using the Simionescu visual three-step technique (iris periphery, collarette and iris freckling). Established risk factors for skin cancer in patients with transplants were recorded as confounding factors. This was an observational cross-sectional study. Among the 110 RTRs, 31 participants had skin cancer. In the skin cancer group, iris periphery was blue/grey in 74% (P = 0.053, odds ratio 2.5), the collarette was light brown in 57% (P < 0.004) and iris freckles were present in 55% (P = 0.04). Dark brown and blue collarettes were observed in controls. Binary logistic regression analysis showed that light brown collarette is a significant independent risk factor for skin cancer (odds ratio 4.54, confidence interval 1.56–10.6, P = 0.02). Within this RTR population a blue iris periphery, light brown collarette and presence of freckling confer an independent risk for skin cancer. Iris pattern is a useful tool for identification of transplant patients at risk of skin cancer and an easy-to-use technique for risk evaluation in this cohort. This is the first study to investigate iris pattern and skin cancer risk in RTRs.
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Schmid, Natalia A., Matthew C. Valenti, Katelyn M. Hampel, et al. "Uniqueness of Iris Pattern Based on the Auto-Regressive Model." Sensors 24, no. 9 (2024): 2797. http://dx.doi.org/10.3390/s24092797.
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In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman’s to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.
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Hiraoka, Toru. "Generating Checkered Pattern Animations Using Inverse Iris Filter." Journal of the Institute of Industrial Applications Engineers 6, no. 1 (2018): 17–20. http://dx.doi.org/10.12792/jiiae.6.17.
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Dincă Lăzărescu, A. M., S. Moldovanu, and L. Moraru. "Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features." Journal of Physics: Conference Series 2701, no. 1 (2024): 012006. http://dx.doi.org/10.1088/1742-6596/2701/1/012006.
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Abstract A combination between the information extracted for both right iris and left iris could increase the efficacy of the biometric recognition systems. In this paper, we propose a biometric identification method based on density of image patterns extracted from human iris images and the combination and comparison of the right iris and the left iris characteristics. The density of the patters approach for processed images can be a new biometric feature used to implement a biometric recognition system with high performance when a small feature dimension is used. In this way, we can maximize the retention of the effective biometric information. The experiments were conducted on the MMU Iris Database containing 225 images of the left eye and 225 images of the right eye. Two morphological Top-hat and Hit or Miss transforms were implemented to find out the particular pattern of pixels. They allow for the enhancement of detail in images. Then, a statistical feature extraction technique is employed to derive the density of the patterns in morphological transformed images. To assess the density of the patterns differences between the right and left iris data groups, the Pearson’s correlation coefficient (PCC) is computed. We reported very good results with a PCC of 0.6164 (strong and positive correlation) for Top-hat morphological operation whilst the Hit or Miss transform returns a PCC of 0.0127 so there is no relationship between the density of the patterns in the right and left irises.
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Hasibuan, Putry Hetty. "Penerapan Jaringan Syaraf Tiruan Untuk Identifikasi Citra Iris Mata Menggunakan Algoritma Delta Rule." Bulletin of Data Science 4, no. 1 (2024): 28–37. https://doi.org/10.47065/bulletinds.v4i1.6414.
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The development of technology today has greatly influenced the development of science, one of which is in the recognition of iris patterns. When compared to fingerprints, the iris has the advantage of being protected by the eyelids and is more stable as the human age increases. The iris in human vision functions to regulate the size of the pupil and regulate the amount of light entering the eye. If observed more deeply the iris has unique characteristics of each individual. so that the iris can be used as a biometric mark for identification. Artificial Neural Network (ANN) is a tool to solve problems, especially in the field and iris pattern recognition. In general, Artificial Neural Network has a working principle that mimics the human neural network system, weighs the actions to be taken, and makes decisions like humans. Iris recognition can be used as an alternative if the introduction of fingerprints as a biometric identity fails. in this study, iris recognition uses the Dela Rule algorithm. The Delta Rule algorithm has the advantage of being able to check errors during the learning process. This will certainly make the Delta Rule algorithm have a high level of accuracy in iris pattern recognition.
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MAnn, Ida. "Iris Pattern in the Vertebrates." Transactions of the Zoological Society of London 21, no. 4 (2010): 355–412. http://dx.doi.org/10.1111/j.1096-3642.1931.tb00659.x.
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Elviralita, Yoan, and Asrul Hidayat. "IDENTIFIKASI POLA IRIS MENGGUNAKAN METODE BACKPROPAGATION." Manutech : Jurnal Teknologi Manufaktur 8, no. 02 (2019): 43–48. http://dx.doi.org/10.33504/manutech.v8i02.21.
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In recent years, there has been a lot of research related to pattern recognition is conducted to identify various forms of patterns and controlling system. Utilizing backpropagation neural network in pattern identifying is very useful to solve problems with unknown parameter and difficult to determined. And then the data of the pattern are trained and tested. The results obtained from the recognition rate indicates a backpropagation neural network, provide excellent performance, which is an average of 98%. This neural network is expected to be developed by other researchers for the advancement of knowledge in all fields.
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Malinowski, Kamil, and Khalid Saeed. "Iris recognition based on local grey extremum values with CNN-based approaches." Machine Graphics and Vision 32, no. 3/4 (2023): 205–32. http://dx.doi.org/10.22630/mgv.2023.32.3.11.
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One of the most important steps in the operation of biometric systems based on iris recognition of the human eye is pattern comparison. However, the comparison of the recorded pattern with the pattern stored in the database of the biometric system cannot function properly without effective extraction of key features from the iris image. In the presented work, we propose an iris recognition system based on image feature extraction and extreme grey shade analysis. Harris-Laplace, RANSAC and SIFT descriptor algorithms were used to find and describe key points. In the experimental part, two methods were used to compare descriptors: the Brute Force method and the Siamese Network method. IIT Delhi Iris Database (version 1.0), MMU v2 database, UBIRIS v1, UBIRIS v2 image databases were used for the study. The proposed method utilizes a different approach when using the generalized corner extraction algorithm (Harris-Laplace algorithms) for comparing iris patterns. In addition, we prove that the use of the descriptor and the Siamese neural networks significantly improves the results obtained in the original method based on paths alone in the case of well contrasted infrared images with very low resolutions.
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Lee, Eui Chul, and Kang Ryoung Park. "Fake iris detection based on 3D structure of iris pattern." International Journal of Imaging Systems and Technology 20, no. 2 (2010): 162–66. http://dx.doi.org/10.1002/ima.20227.
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Hicham, Ohmaid, Eddarouich S., Bourouhou A., and Timouyas M. "Iris segmentation using a new unsupervised neural approach." International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 58–64. https://doi.org/10.11591/ijai.v9.i1.pp58-64.
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A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristic possessed by an individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris. Owing to the unique epigenetic patterns of the iris, Iris recognition is considered as one of the most accurate methods in the field of biometric identification. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction the iris. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the center and radius of the pupil and the iris.
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Šemeklis, Lukas, Laura Kapitanovaitė, Grinvydas Butrimas, et al. "Iris Pigmented Lesions and Risk of Cutaneous Melanoma: Case–Control Study in Lithuania." Journal of Personalized Medicine 14, no. 5 (2024): 530. http://dx.doi.org/10.3390/jpm14050530.
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The global incidence of cutaneous melanoma (CM) is rising, necessitating early detection and identification of risk factors across different populations. A case–control study with 180 patients with primary diagnosed CM and 182 healthy controls was conducted. Participants underwent ophthalmic and skin examinations, where the identification and counting of common melanocytic nevi (CMN) and atypical melanocytic nevi (AMN) was performed. During ophthalmic examination, high-resolution slit lamp iris images were taken. Images were categorized according to iris periphery, collaret, and freckles. There was no difference in iris periphery and collaret color between groups. However, blue/grey iris periphery and blue collaret with or without freckles were the most common patterns. The presence of pigmented iris lesions and 2–5 mm and ≥5 mm in diameter CMNs was strongly associated with CM risk. The evidence from this study indicates that blue or grey periphery and blue collaret iris pattern with iris freckles are 2.74 times higher in the CM group than controls. Further research is needed to explore iris patterns’ association with CM risk in diverse populations.
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Khaire, Saurabh Namdev. "“Biometric Attendance Monitoring Using Iris Recognition Technology"." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04112.
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Abstract - In modern institutions and organizations, ensuring accurate and secure attendance tracking remains a significant challenge. Traditional methods such as manual roll calls or card-based systems are often prone to errors, fraud, and inefficiencies. Biometric-based attendance monitoring system utilizing iris recognition technology, offering a secure, non-invasive, and highly accurate solution biometric attendance monitoring system based on iris recognition technology. The proposed system offers a secure, efficient, and contactless method of recording attendance, overcoming the limitations of traditional methods such as manual entry and RFID cards. By utilizing the uniqueness and stability of the human iris, the system ensures high accuracy and resistance to spoofing. The research discusses the design, development, and performance evaluation of the system, incorporating image processing and pattern recognition algorithms. Experimental results demonstrate the system's effectiveness in real-world conditions, highlighting its potential for applications in educational institutions, corporate offices, and secure facilities. The proposed system captures and verifies the unique iris patterns of individuals to record attendance in real-time. Leveraging image processing and pattern recognition algorithms, the system ensures high matching accuracy while mitigating the risk of impersonation. Key Words: Biometric authentication, Iris recognition, Attendance system, Image processing, Pattern recognition, Security, Automation.
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Yogita Deepak Sinkar. "Iris Detection and Authentication System Using Deep Learning Techniques." Panamerican Mathematical Journal 35, no. 2s (2024): 169–86. https://doi.org/10.52783/pmj.v35.i2s.2425.
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A biometric modality for individual identification, iris recognition is very reliable because human iris patterns are stable and distinctive. An overview of a unique deep learning method for iris recognition and authentication using convolutional neural networks (CNN) is provided in this abstract. The suggested approach provides a reliable and safe means of personal verification by utilizing Convolutional neural networks to extract complex information from iris pictures. The first step in the iris identification procedure is to get a person's iris image, which is usually done with the use of specialist iris imaging equipment. To improve the clarity of the iris pattern and reduce noise, the obtained iris picture is pre-processed. To generate a uniform template for additional processing, the iris region is then separated and isolated from the remainder of the picture. Using Convolutional neural network deep learning for iris detection has several benefits, such as high accuracy, resilience to spoof attacks, and adaptability to changes in illumination and pupil dilation. Applications like financial transactions, border security, and secure access management are just a few of the areas in which technology excels.
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WANG, ZHAOBIN, YIDE MA, and GUANGZHU XU. "A NEW APPROACH TO IRIS RECOGNITION." International Journal of Information Acquisition 04, no. 01 (2007): 69–76. http://dx.doi.org/10.1142/s0219878907001174.
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Iris recognition technology relates to computer vision, pattern recognition, statistical inference, and optics etc. Because the randomness of iris patterns has very high dimensionality, it is difficult to find an efficient approach to iris recognition. The intersecting cortical model (ICM) is good at directly extracting important information from image. A new method of iris recognition is proposed for the first time based on the ICM. With the method, a series of binary images are firstly produced from iris image through the ICM. Entropy sequence can be gained from these binary images. And then phase information is obtained from entropy sequence. This phase information is taken as feature vector because of its uniqueness. The results show that our method is feasible, potential and effective in obtaining feature from iris image.
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Connor, Brian O’, and Kaushik Roy. "Iris Recognition Using Level Set and Local Binary Pattern." International Journal of Computer Theory and Engineering 6, no. 5 (2014): 416–20. http://dx.doi.org/10.7763/ijcte.2014.v6.901.
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A. Kumar, Shruthi, and A. Baskar. "Improving the IRIS Recognition Under Different Illumination Using RETINEX Algorithms." International Journal of Engineering & Technology 7, no. 3.6 (2018): 81. http://dx.doi.org/10.14419/ijet.v7i3.6.14943.
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Iris detection and recognition provides more accurate and secure authentication nowadays. The probability of any two people having the same iris pattern is nearly zero, even the identical twins will not have the same iris pattern. The noise and illumination changes, challenges iris recognition correctness and security in authentication process. The available recent pre-processing techniques for iris detection address different type of noise suppression and removing unwanted information in iris, but still it strives with illumination issues. In this paper, we proposed Retinex algorithm for improving iris detection rate. The proposed work comprises into three steps: First we proposed Retinex algorithm in pre-processing, it works based on reflectance value of image and skips the illumination value in image, subsequently feature extraction uses Gabor filter for iris code generation. In conclusion, distance metrics Hamming distance used for iris recognition the proposed work evaluated MMU iris database under different illumination conditions and provides better results.
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Shirke, Swati D., and C. Rajabhushnam. "Local gradient pattern and deep learning-based approach for the iris recognition at-a-distance." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 1 (2021): 49–64. http://dx.doi.org/10.3233/kes-210052.
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One of the biometric techniques utilized to predict the human is based on the iris. The recognition of iris is performed by discovering an individual without human intervention utilizing the iris of human eyes. Iris offers distinct information about the person. This research presents deep learning strategy for performing iris recognition. Primarily, image is pre-processed to obtain exact region iris. Then, region of iris is extracted using Hough Transform, followed by segmentation and normalization of iris region using Daugman’s rubber sheet model. Once segmentation is performed, the features are generated with ScaT-LOOP that is the combination of Scattering Transform (ST), Tetrolet transforms (TT), Local Gradient Pattern (LGP) and Local Optimal Oriented Pattern (LOOP). Finally, steepest gradient-based Deep Belief Network (DBN) is utilized for recognizing the iris. The performance of iris recognition using the DBN classifier is computed based on accuracy, False Rejection Rate (FRR), and False Acceptance Rate (FAR). The proposed method achieves maximum accuracy of 97.96%, minimal FAR of 0.493%, and minimal FRR of 0.48% that indicates its superiority.
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Ives, Robert W. "Use of one-dimensional iris signatures to rank iris pattern similarities." Optical Engineering 45, no. 3 (2006): 037201. http://dx.doi.org/10.1117/1.2181140.
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Rajkumar, D. "IRIS Species Predictor." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1530–35. http://dx.doi.org/10.22214/ijraset.2022.40097.
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Abstract: In Machine Learning, we are using semi-automated extraction of knowledge of data for identifying IRIS flower species. Classification is a supervised learning in which the response is categorical that is its values are in finite unordered set. To simply the problem of classification, scikit learn tools have been used. This paper focuses on IRIS flower classification using Machine Learning with scikit tools. Here the problem concerns the identification of IRIS flower species on the basis of flowers attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS flower and how the prediction was made from analyzing the pattern to from the class of IRIS flower. In this paper we train the machine learning model with data and when unseen data is discovered the predictive model predicts the species using what it has been learnt from the trained data. Keywords: MATLAB, Machine learning, Neural Network.
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Hosseini, Mahdi S., Babak N. Araabi, and Hamid Soltanian-Zadeh. "Pigment Melanin: Pattern for Iris Recognition." IEEE Transactions on Instrumentation and Measurement 59, no. 4 (2010): 792–804. http://dx.doi.org/10.1109/tim.2009.2037996.
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Hicham, Ohmaid, Eddarouich S., Bourouhou A., and Timouyas M. "Comparison between SVM and KNN classifiers for iris recognition using a new unsupervised neural approach in segmentation." International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (2020): 429–38. https://doi.org/10.11591/ijai.v9.i3.pp429-438.
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A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristics he or she possesses. Iris recognition is a biometric identification method that applies pattern recognition to images of the iris. Owing to the unique epigenetic patterns of the iris, iris recognition is considered one of the most accurate methods in the field of biometric identification. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the center and radius of the pupil and the iris. Then the normalization allows transforming the segmented circular iris region into a fixed-size rectangular shape using Daugman’s rubber sheet model. A discrete wavelet transformation (DWT) is applied to the normalized iris to lower the size of iris models and to improve classifier accuracy. Finally, the URIBIS iris database is used for individual user verification by using the KNN classifier or support vector machine (SVM) which based on the analysis of iris code as feature extraction is discussed.
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Pontoh, Fransisca Joanet, Fransiscus Xaverius Senduk, and Inggrit E. G. Pondaag. "APLIKASI PENGENALAN IRIS MATA MENGGUNAKAN METODE HOUGH TRANSFORM DAN GABOR WAVELET." JURNAL ILMIAH INFORMATIKA 9, no. 02 (2021): 105–9. http://dx.doi.org/10.33884/jif.v9i02.4205.
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Biometric system is a development of the basic method of identification system by using the characteristics of humans as it’s object. These include face, fingerprints, signature, palms, iris, ears, sounds even DNA. Face recognition is one of the identification techniques in biometrics that uses part of the face as its parameter. One of the biometric parts of face is Iris. Iris is a unique part of the eyes, this is because the pattern of the somebody eyes will be quite different from the other, even genetically identical twins have different iris patterns. This research will use the Hough and Gabor method to perform iris recognition. The results show that the application has succeeded in recognizing the selected eye image if the eye image is registered in the database.
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Ohmaid, Hicham, S. Eddarouich, A. Bourouhou, and M. Timouyas. "Iris segmentation using a new unsupervised neural approach." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 58. http://dx.doi.org/10.11591/ijai.v9.i1.pp58-64.
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<span lang="EN-GB">A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristic possessed by an individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris. Owing to the unique epigenetic patterns of the iris, Iris recognition is considered as one of the most accurate methods in the field of biometric identification. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction the iris. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the </span><span lang="EN-US">center</span><span lang="EN-GB"> and radius of the pupil and the iris.</span>
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Hamd, Muthana, and Samah Ahmed. "A Biometric System for Iris Recognition Based on Fourier Descriptors and Principle Component Analysis." Iraqi Journal for Electrical and Electronic Engineering 13, no. 2 (2017): 180–87. http://dx.doi.org/10.37917/ijeee.13.2.5.
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Iris pattern is one of the most important biological traits of humans. In last years, the iris pattern is used for human verification because of uniqueness of its texture. In this paper, biometric system based iris recognition is designed and implemented using two comparative approaches. The first approach is the Fourier descriptors, in this method the iris features have been extracted in frequency domain, where the low spectrums define the general description of iris pattern, while the high spectrums describes the fine detail. The second approach, the principle component analysis uses statistic technique to select the most important feature values by reducing its dimensionality. The biometric system is tested by applying one-to-one pattern matching procedure for 50 persons. The distance measurement method is applied for Manhattan, Euclidean, and Cosine classifiers for purpose of comparison. In all three classification methods, Fourier descriptors were always advanced principle component analysis in matching results. It satisfied 96%, 94%, and 86% correct matching against 94%, 92%, and 80% for principle component analysis using Manhattan, Euclidean, and Cosine classifiers respectively.
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Mo, Mo Myint Wai, Phyo Aung Nyan, and Lwin Htay Lwin. "Software Implementation of Iris Recognition System using MATLAB." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 290–95. https://doi.org/10.5281/zenodo.3589730.
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The software implementation of iris recognition system introduces in this paper. This system intends to apply for high security required areas. The demand on security is increasing greatly in these years and biometric recognition gradually becomes a hot field of research. Iris recognition is a branch of biometric recognition method. In thesis, Iris recognition system consists of localization of the iris region and generation of data set of iris images followed by iris pattern recognition. In thesis, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network NN is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The implementation of the system is developed with MATLAB. The results of simulations illustrate the effectiveness of the neural system in personal identification. Finally, the accuracy of iris recognition system is tested and evaluated with different iris images. Mo Mo Myint Wai | Nyan Phyo Aung | Lwin Lwin Htay "Software Implementation of Iris Recognition System using MATLAB" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25258.pdf
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Jin, Nan, Sébastien Mavromatis, Jean Sequeira, and Stéphane Curcio. "A Robust Method of Eye Torsion Measurement for Medical Applications." Information 11, no. 9 (2020): 408. http://dx.doi.org/10.3390/info11090408.
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The detection of eye torsion is an important element for diagnosis of balance disorders, although it is rarely available in existing eye tracking systems. A novel method is proposed in this paper to provide robust measurement of torsional eye movements. A numerical approach is presented to estimate the iris boundary only according to the gaze direction, so the segmentation of the iris is more robust against occlusions and ambiguities. The perspective distortion of the iris pattern at eccentric eye positions is also corrected, benefiting from the transformation relation that is established for the iris estimation. The angle of the eye torsion is next measured on the unrolled iris patterns via a TM (Template Matching) technique. The principle of the proposed method is validated and its robustness in practice is assessed. A very low mean FPR (False Positive Rate) is reported (i.e., 3.3%) in a gaze test when testing on five participants with very different eye morphologies. The present method always gave correct measurement on the iris patterns with simulated eye torsions and rarely provided mistaken detections in the absence of eye torsion in practical conditions. Therefore, it shows a good potential to be further applied in medical applications.
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Yohandy, Daniel Hadrian, I. Made Normo Wiranata, and Tea Qaula Ferbia. "Identifikasi Pola Penyakit Pada Citra Iris Mata dengan RBF Neural Network." Jurnal Informatika 5, no. 2 (2018): 195–201. http://dx.doi.org/10.31311/ji.v5i2.3783.
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AbstrakGaya kehidupan manusia semakin beragam dan padat sehingga banyak terjadi dimana penyakit dan gangguan pada tubuh diketahui ketika sudah dalam kondisi parah. Kebanyakan manusia tidak disiplin dalam memantau kesehatan mereka karena dirasa belum ada cara yang mudah dan efisien, cara yang mayoritas digunakan adalah check-up ke rumah sakit. Iris mata manusia dapat menjadi salah satu media atau sarana dalam mengenali gangguan kondisi tubuh seseorang. Pola yang muncul pada iris mata dapat dikenali dengan bantuan teknologi jaringan saraf tiruan yang salah satunya adalah metode RBF yang dipakai dalam penelitian ini. Hasil yang ditemui pada penelitian ini adalah penggunaan metode RBF mampu untuk mengenali pola yang ada di iris mata, dan dapat memberikan hasil pengenalan penyakit kompleks dan stress dengan akurasi yang dibilang masih rata-rata (sekitar 50%). Kata Kunci: Penyakit, Iris Mata, Pola, RBF AbstractHuman lifestyle is more diverse and dense, so much happening where disease and disturbance in the body are known when it is in severe condition. Most humans are not disciplined in monitoring their health because it feels there is no easy and efficient way, the way that the majority used is the check-up to the hospital. The iris of the human eye can be one of the media or means in recognizing a person's body condition disorder. Patterns that appear on the iris can be identified with the help of artificial neural network technology one of which is the RBF method used in this study. The results found in this study are the use of RBF method to recognize the existing pattern in the iris and can provide the results of the introduction of complex diseases and stress with an accuracy that is still fairly average (about 50%). Keywords: Disease, Eye Iris, Pattern, RBF
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Yohandy, Daniel Hadrian, I. Made Normo Wiranata, and Tea Qaula Ferbia. "Identifikasi Pola Penyakit Pada Citra Iris Mata dengan RBF Neural Network." Jurnal Informatika 5, no. 2 (2018): 195–201. http://dx.doi.org/10.31294/ji.v5i2.3783.
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AbstrakGaya kehidupan manusia semakin beragam dan padat sehingga banyak terjadi dimana penyakit dan gangguan pada tubuh diketahui ketika sudah dalam kondisi parah. Kebanyakan manusia tidak disiplin dalam memantau kesehatan mereka karena dirasa belum ada cara yang mudah dan efisien, cara yang mayoritas digunakan adalah check-up ke rumah sakit. Iris mata manusia dapat menjadi salah satu media atau sarana dalam mengenali gangguan kondisi tubuh seseorang. Pola yang muncul pada iris mata dapat dikenali dengan bantuan teknologi jaringan saraf tiruan yang salah satunya adalah metode RBF yang dipakai dalam penelitian ini. Hasil yang ditemui pada penelitian ini adalah penggunaan metode RBF mampu untuk mengenali pola yang ada di iris mata, dan dapat memberikan hasil pengenalan penyakit kompleks dan stress dengan akurasi yang dibilang masih rata-rata (sekitar 50%). Kata Kunci: Penyakit, Iris Mata, Pola, RBF AbstractHuman lifestyle is more diverse and dense, so much happening where disease and disturbance in the body are known when it is in severe condition. Most humans are not disciplined in monitoring their health because it feels there is no easy and efficient way, the way that the majority used is the check-up to the hospital. The iris of the human eye can be one of the media or means in recognizing a person's body condition disorder. Patterns that appear on the iris can be identified with the help of artificial neural network technology one of which is the RBF method used in this study. The results found in this study are the use of RBF method to recognize the existing pattern in the iris and can provide the results of the introduction of complex diseases and stress with an accuracy that is still fairly average (about 50%). Keywords: Disease, Eye Iris, Pattern, RBF
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Azimi, Mohammadreza, Seyed Ahmad Rasoulinejad, and Andrzej Pacut. "Iris recognition under the influence of diabetes." Biomedical Engineering / Biomedizinische Technik 64, no. 6 (2019): 683–89. http://dx.doi.org/10.1515/bmt-2018-0190.
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Abstract In this study, iris recognition under the influence of diabetes was investigated. A new database containing 1318 pictures from 343 irides – 546 images from 162 healthy irides (62% female users, 38% male users, 21% <20 years old, 61% (20) < 40 years old, 12% (40) <60 years old and 6% more than 60 years old) and 772 iris images from 181 diabetic eyes but with a clearly visible iris pattern (80% female users, 20% male users, 1% <20 years old, 17.5% (20) <40 years old, 46.5% (40) <60 years old and 35% more than 60 years old) – were collected. All of the diabetes-affected eyes had clearly visible iris patterns without any visible impairments and only type II diabetic patients with at least 2 years of being diabetic were considered for the investigation. Three different open source iris recognition codes and one commercial software development kit were used for achieving the iris recognition system’s performance evaluation results under the influence of diabetes. For statistical analysis, the t-test and the Kolmogorov-Simonov test were used.
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Wattjes, Mike P., Martijn T. Wijburg, Jeroen van Eijk, et al. "Inflammatory natalizumab-associated PML: baseline characteristics, lesion evolution and relation with PML-IRIS." Journal of Neurology, Neurosurgery & Psychiatry 89, no. 5 (2017): 535–41. http://dx.doi.org/10.1136/jnnp-2017-316886.
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Background and objectiveNatalizumab-associated progressive multifocal leukoencephalopathy (NTZ-PML) patients may show imaging signs suggestive of inflammation at diagnosis (‘inflammatory PML’), reminiscent of PML-immune reconstitution inflammatory syndrome (PML-IRIS). We investigated the imaging characteristics of inflammatory NTZ-PML lesions and PML-IRIS to determine differentiating and overlapping features.MethodsWe scored the presence, localisation and pattern of imaging characteristics of inflammation on brain MRI scans of inflammatory NTZ-PML patients. The imaging characteristics were followed up until the occurrence of PML-IRIS.ResultsTen out of the 44 NTZ-PML patients included showed signs suggestive of inflammation at the time of diagnosis. The inflammation pattern at diagnosis was similar to the pattern seen at PML-IRIS, with contrast enhancement representing the most frequent sign of inflammation (90% at diagnosis, 100% at PML-IRIS). However, the severity of inflammation differed, with absence of swelling and low frequency of perilesional oedema (10%) at diagnosis, as compared with the PML-IRIS stage (40%).ConclusionPatterns of inflammation at the time of PML diagnosis and at the PML-IRIS stage overlap but differ in their severity of inflammation. This supports histopathological evidence that the inflammation seen at both stages of the same disease shares a similar underlying pathophysiology, representing the immune response to the JC virus to a variable extend.
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Qadir, Tara Othman, Nik Shahidah Taujuddin, and Norfaiza Fuad. "A New Feature Extraction Approach in Classification for Improving the Accuracy in Iris Recognition." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2161. http://dx.doi.org/10.62527/joiv.7.4.1373.
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Personal identity is becoming increasingly vital to meet the increasing security standards of today's business society. Iris recognition is one of the most accurate biometric technologies currently in use. Iris recognition is employed in high-security sectors due to its dependability and flawless identification rates. The steps of iris identification, comprising image preparation, extraction of features, and classifier creation, are described thoroughly in the primary portion of this research. The feature extraction stage is the most important in an iris identification system since it extracts the iris's distinctive feature. Several methods have been devised to extract the various characteristics that are unique to everyone. Modern iris identification systems frequently use Gabor filters to identify iris textural characteristics. However, in the application, it is necessary to identify the appropriate Gabor modules and to generate a pattern of iris Gabor characteristics. This research aims to provide a novel multi-channel Gabor filter and Wavelet filter for breaking down and extracting iris data from two different iris datasets. Because wavelet is the most scalable method of image processing, the research investigates using it to create a unique pattern for the iris recognition system. The MATLAB program is used to implement these ideas. CASIA and MMU are the datasets used for this purpose, and their comparative analysis is addressed in the research. To show how well the method performs, experimental results are given. We demonstrate through experiments that the suggested approach results in excellent iris identification performance.
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Jeong, Dae Sik, Jae Won Hwang, Byung Jun Kang, et al. "A new iris segmentation method for non-ideal iris images." Image and Vision Computing 28, no. 2 (2010): 254–60. http://dx.doi.org/10.1016/j.imavis.2009.04.001.
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Kiseleva, T. N., S. V. Saakyan, V. V. Makukhina, et al. "Optical coherence tomography-angiography for anterior uveal tract evaluation in normal subjects and in pathology." Russian Ophthalmological Journal 16, no. 4 (2023): 35–43. http://dx.doi.org/10.21516/2072-0076-2023-16-4-35-43.
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Purpose: to assess the potentials of optical coherence tomography-angiography (OCTA) in the evaluation of anterior segment (AS) uveal vessels in normal and pathological conditions.Material and methods. 20 healthy volunteers (40 eyes) with no ophthalmic pathology (the control group) and 85 previously untreated patients (85 eyes) with suspected tumors of the irido-ciliary area (the iris, the ciliary body and the peripheral sections of the choroid) were examined. All participants had optical coherence tomography (OCT) and OCTA of AS, with the qualitative assessment of scans (vessels pattern, lumen, tortuosity) and the quantitative assessment (vessel density index, VD%, for the affected zone). In ciliary body or choroid pathologies, VD index was measured for the conjunctiva in the focus projection. VD index included mean and local VD, as well as VD of perifocal tissues. In addition, B-mode ultrasound scanning and ultrasound biomicroscopy were performed.Results. OCTA scans in 4 sectors of the normal iris showed a predominantly radial pattern of conjunctival vessels positioning, with their lumen remaining the same along their entire visible length. The lowest VD value (38.5%) was registered in the temporal iris segment, while the highest (43.9%) was revealed in the inferior quadrant. In the iris tumors area, intrinsic vascularity molded in various vascular patterns was observed. Melanomas could be suspected because of the vessels oriented along the axis of the tumor and by non-uniform lumen of the vessels.Conclusion. AS-OCTA is an informative method for the visualization of iris vessels in normal conditions and in iris pathology and may be considered a valuable addition to the standard visualization techniques.
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Johny, Anil, and Siyamol Chirakkarottu. "Secure Encryption Method for Biometric Iris Pattern." International Journal of Computer Trends and Technology 12, no. 5 (2014): 217–19. http://dx.doi.org/10.14445/22312803/ijctt-v12p143.
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Yáñez, Claudio, Juan E. Tapia, Claudio A. Perez, and Christoph Busch. "Impact of Occlusion Masks on Gender Classification from Iris Texture." IET Biometrics 2024 (January 27, 2024): 1–13. http://dx.doi.org/10.1049/2024/8526857.
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Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.
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Olatunbosun, A., and O. Bamigboye. "An Iris Recognition and Detection System Implementation." Publisher 5, no. 8 (2020): 8–10. https://doi.org/10.35940/ijies.H0958.025820.
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There was big interest in the use of biometrical identification techniques, such as iris, face, fingerprints, ears, and significant technological developments and to enhance safety issues. The application varies depending on the location, based on the resources. They are used in safety at airports, border safety, criminal investigation, and so on. This study focuses on iris-based biometric technology. Biometric technology based on the iris diaphragm is the most reliable and acceptable among other biometric technologies. In this study, we developed the IRIS graphic user interface and attempted to use the streamlined segmentation technique to create a simpler and efficient way to detect iris. The ' Matlab ' software tool is being used to fix the recognized issues when implementing the produced code using suitable new algorithms. The proposed system is not just used to eliminates noises but also enables the border between the iris and the pupil to be correctly established. Results are saved in a computer with the corresponding model steps are performed using neural networks and synthesis algorithms. Pattern compatibility uses the appropriate metric to compare custom patterns with database patterns. The match option shows the measure of similarity between two diaphragm patterns. Finally, it is a strong level of trust that determines whether the user is authenticated or defined. As a binary template referred to as iris code the output of the Gabor wavelet (real and imaginary) is quantized as a stage. The FAR and FRR resulting from that are 0.001% and 37,880%.
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Liu, Jin, Chang Ming Liu, and Yan Jun Sun. "Iris Feature Extraction Based on the Fuzzy Clustering Evaluation Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 3412–15. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3412.
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In the iris recognition, as the texture feature of iris is complicated , and it is affected by the acquisition environment, time, condition and other factors, so it is difficult to express its complete information in single features of iris. Therefore, the combination of a variety of features or integration of expression and recognition method becomes a new kind of choice. Information integration concept originated in the 1970s. In recent years, because of the gradually in-depth recognition of people, the application of information integration technology in the pattern recognition becomes a hot spot of the research, especially when the single pattern cannot complete various aspects of requirements well in the pattern recognition, the adoption of integration technology can integrate various aspects of information provided by multi-pattern features, so as to obtain more comprehensive and accurate information of the recognized objects and overcome the limitations of recognition method through single-pattern feature, thus getting a satisfactory effect.
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Arnold, Michael L., Bobby D. Bennett, and Elizabeth A. Zimmer. "Natural Hybridization between Iris fulva and Iris hexagona: Pattern of Ribosomal DNA Variation." Evolution 44, no. 6 (1990): 1512. http://dx.doi.org/10.2307/2409333.
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Arnold, Michael L., Bobby D. Bennett, and Elizabeth A. Zimmer. "NATURAL HYBRIDIZATION BETWEEN IRIS FULVA AND IRIS HEXAGONA : PATTERN OF RIBOSOMAL DNA VARIATION." Evolution 44, no. 6 (1990): 1512–21. http://dx.doi.org/10.1111/j.1558-5646.1990.tb03842.x.
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Swati D. Shirke, Ms, and Dr C. Rajabhushanam. "Iris Feature Extraction Methods Overview." International Journal of Engineering & Technology 7, no. 4.39 (2018): 90–93. http://dx.doi.org/10.14419/ijet.v7i4.39.23713.
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Iris reorganization remains one of the superlative recognition techniques in Biometrics system, for human Identification and authentication purpose we can use IRIS Recognition technique by using machine learning technologies. Machine Learning helps us find solutions of many problems in computer vision and recognition techniques [1] .Iris recognition task not only effortlessly but also every day we recognize our friends, relative as well as family members. We also recognition by using persons IRIS pattern composed of a particular combination of features. The main process in IRIS Recognition system is feature learning i.e. a set of techniques that learn feature [2][3]. This Paper deals with: Dimension Reduction techniques for IRIS feature Extraction.  Â
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Kintonova, Aliya, Igor Povkhan, Marzhan Mussaif, and Galymzhan Gabdreshov. "Improvement of iris recognition technology for biometric identification of a person." Eastern-European Journal of Enterprise Technologies 6, no. 2 (120) (2022): 60–69. http://dx.doi.org/10.15587/1729-4061.2022.269948.
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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
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Aliya, Kintonova, Povkhan Igor, Mussaif Marzhan, and Gabdreshov Galymzhan. "Improvement of iris recognition technology for biometric identification of a person." Eastern-European Journal of Enterprise Technologies 6, no. 2 (120) (2022): 60–69. https://doi.org/10.15587/1729-4061.2022.269948.
Full textAbstract:
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
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RAJA SEKAR, J., S. ARIVAZHAGAN, S. SHOBANA PRIYADHARSHINI, and S. SHUNMUGAPRIYA. "IRIS RECOGNITION USING COMBINED STATISTICAL AND CO-OCCURRENCE MULTI-RESOLUTIONAL FEATURES." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 01 (2013): 1356001. http://dx.doi.org/10.1142/s0218001413560016.
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Iris recognition is one of the most reliable personal identification methods. This paper presents a novel algorithm for iris recognition encompassing iris segmentation, fusion of statistical and co-occurrence features extracted from the curvelet and ridgelet transformed images. In this work, the pupil and iris boundaries are detected by using the equation of circle from three points on its circumference. Using Canny edge detection, the iris radius value is empirically chosen based on rigorous experimentation. Eyelash removal is done by using a horizontal 1-D rank filter. Iris normalization is done by mapping the detected iris region from the polar domain to the rectangular domain and the multi-resolution transforms such as curvelet and ridgelet transforms are applied for multi-resolutional feature extraction. The classification is done using Manhattan distance (Md) and multiclass classifier with logistic function and the two results are compared. Here, the benchmark database CASIA-IRIS-V3 (Interval) is used for identification and recognition. It is observed that the ridgelet transform increases the iris recognition rate.
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WANG, ZHIFANG, QI HAN, and CHRISTOPH BUSCH. "A NOVEL IRIS LOCATION ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 01 (2009): 59–70. http://dx.doi.org/10.1142/s0218001409007028.
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Biometric technologies are becoming much more important in various applications. Among them, iris recognition is considered as one of the most reliable and accurate technologies. In the preparation of iris recognition, the iris location will influence the performance of the entire system. This paper proposes a novel algorithm to locate iris and eyelids. Morphological operation is applied to remove eyelashes during iris boundary location. An optimal step length is calculated to reduce the searching time. Experimental results demonstrate that the proposed iris location algorithm is able to achieve a good performance with accuracy higher than 97.6%.