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Journal articles on the topic 'Iris recognition system'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Bui, Hoang Hai, and Jr-Jen Huang. "A NOVEL LOW-COST IRIS RECOGNITION SYSTEM." International Journal of Psychosocial Rehabilitation 24, no. 04 (2020): 572–83. http://dx.doi.org/10.37200/ijpr/v24i4/pr201035.

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2

Meenakshi BK, Meenakshi BK, and Prasad M. R. Prasad M R. "Survey on Segmentation to Iris Recognition System." International Journal of Scientific Research 3, no. 4 (2012): 514–15. http://dx.doi.org/10.15373/22778179/apr2014/184.

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3

Thomas, Tossy, Anu George, and K. P. Indira Devi. "Effective Iris Recognition System." Procedia Technology 25 (2016): 464–72. http://dx.doi.org/10.1016/j.protcy.2016.08.133.

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4

Orugba, Obokparo, and Anthony Imianvan. "ATTENDANCE SYSTEM USING IRIS RECOGNITION." Nigerian Journal of Science and Environment 22, no. 1 (2024): 151–60. http://dx.doi.org/10.61448/njse2212412.

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Iris is one of the biometric systems of recognition. Its algorithms provides a high transparency in recognition of individuals. The Iris recognition takes class attendance and attendance of staffs in their working establishment by capturing the image using the iris sensitivity for a matching on the database. A model for attendance recording using Iris Recognition is presented in this paper. This is far better than the traditional/manual method of taking attendance in institutions which is full of mistakes and manipulations

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5

Ali, Musab A. M., Nooritawati Md Tahir, and Israa Al-Rawe. "File Encryption by Iris Recognition System." Jurnal Kejuruteraan 36, no. 5 (2024): 1955–63. http://dx.doi.org/10.17576/jkukm-2024-36(5)-15.

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This paper designs a framework for an iris recognition system to obtain significant information by encryption and decryption using iris recognition system to improve the security level of stored extended data. Iris feature to confirm the character of an individual person to grant access to the information document. The strategy is extracted using 1D filter Log-Gabor which has high decidability and minimum variance between (inter & intra) class. In this paper, the 1D filter Log-Gabor was applied, the results are based on FAR and FRR, as feature selection models with SVM type nonlinear quadr

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6

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

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7

Sridevi, R., and P. Shobana. "Iris Image Preprocessing and Recognition System." International Journal of Current Research and Review 14, no. 06 (2022): 38–42. http://dx.doi.org/10.31782/ijcrr.2022.14606.

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Introduction: Iris recognition framework has gotten vital, particularly in the field of safety, since it gives high reliability. Iris surface is a natural secret phrase that enjoys incredible benefits like inconstancy, soundness, unique highlights for every individual, and its significance in the security field. This makes an iris acknowledgment framework upper of other biometric strategies utilized for human identification. Iris is a hued muscle present inside the eye which helps in controlling the measure of light entering the eye. It has a few extraordinary textural data, which doesn’t get

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8

Prajapati, Harish, and Rajesh M Bodade. "Review on Iris Recognition System for Unconstrained Environment." International Journal of Science and Research (IJSR) 10, no. 1 (2021): 981–91. https://doi.org/10.21275/sr201027094049.

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9

Rana, Humayan Kabir, Md Shafiul Azam, and Mst Rashida Akhtar. "Iris Recognition System Using PCA Based on DWT." SM Journal of Biometrics & Biostatistics 2, no. 3 (2017): 1015. https://doi.org/10.5281/zenodo.2580202.

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The Biometric recognition is the study of identifying individuals based on their unique physiological or behavioral characteristics, includes iris, face, fingerprint, retina, vein, hand geometry, hand writing, human gait, signature, keystrokes and voice. Among the biometrics, an iris has unique structure and it remains stable over a person life time. So that iris recognition is regarded as the most accurate and reliable biometric recognition system. In this paper, we proposed a technique that uses Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for selecting f

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10

Asst.Prof., N.Deepa* V.Priyanka student J.Pradeepa student. "IRIS RECOGNITION BASED ON IRIS CRYPTS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 2 (2017): 408–12. https://doi.org/10.5281/zenodo.291855.

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Iris is a biometric trait used for human recognition in various applications. There is a lack of human friendly techniques for iris comparison. Therefore it has not been reported in forensics applications. We need to capture iris of human and similarities between the irises is captured. Recently Human-in-the-loop system has been developed based on matching and detection of iris crypts. Our detection is able to capture crypts of various sizes and able to identify any kind of topological changes. Presently iris recognition exists in Aadhar card projects. The proposed system of this model is to p

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11

Maram.G, Alaslni1 and Lamiaa A. Elrefaei1 2. "TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS FOR IRIS RECOGNITION." International Journal of Artificial Intelligence & Applications (IJAIA) 10, september (2019): 49–66. https://doi.org/10.5281/zenodo.3541493.

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Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. I

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12

Jenadeleh, Mohsen, Marius Pedersen, and Dietmar Saupe. "Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition." Sensors 20, no. 5 (2020): 1308. http://dx.doi.org/10.3390/s20051308.

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Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based o

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13

Gh, Zainab. "Efficient Method for Iris Recognition System." Wasit Journal for Pure sciences 3, no. 1 (2024): 65–73. http://dx.doi.org/10.31185/wjps.271.

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This article shows and tests a good eye recognition method that works well in image situations with fewer restrictions. The Circular Hough Transform (CHT) and Truncated Total Variation model were used to separate the iris from other parts and noises in an eye picture to locate and separate the iris accurately. This helps get the most exact pictures of the eye. Doughman’s rubber sheet model makes the split eye area regular and normalized. The Principal Component Analysis (PCA) method is used to identify features of iris patterns that come from Eigen irises. A test image is projected onto the su

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14

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

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15

B., Kiran Bala and A. Balakumar. "COMPARISON OF DIFFERENT TRANSFORMATIONS IN IRIS RECOGNITION SYSTEM." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES o6, no. 04 (2019): 8187–91. https://doi.org/10.5281/zenodo.2649020.

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<em>Iris recognition system is a very powerful system and give security to the society and the technology is a trusted one but, to give more strength to the iris recognition system the proposed system deals with comparison of different transformation in authentication mainly focus on false acceptance rate, false rejection rate and time management of the entire process to give effective result of the proposed system justify the best transformation applicable for the iris recognition. In this system for iris recognition system own eye database has been used for the entire process and apply diffe

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Adekunle, Adekunle, Y. A, Aiyeniko Aiyeniko, et al. "Feature Extraction Techniques for Iris Recognition System: A Survey." International Journal of Innovative Research in Computer Science & Technology 8, no. 2 (2020): 37–42. http://dx.doi.org/10.21276/ijircst.2020.8.2.5.

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17

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

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18

Shrivas, Anuradha, and Preeti Tuli. "Effective analysis of Iris Images for Iris Recognition System." International Journal of Science and Engineering Applications 1, no. 1 (2012): 85–88. http://dx.doi.org/10.7753/ijsea0101.1015.

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19

Yuan, Xiaoyan, and Pengfei Shi. "Efficient iris recognition system based on iris anatomical structure." IEICE Electronics Express 4, no. 17 (2007): 555–60. http://dx.doi.org/10.1587/elex.4.555.

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20

Abdullah karim, Abdulamir, and Sarah jasim Mohammed. "Improved Approach to Iris Normalization for iris Recognition System." Engineering and Technology Journal 33, no. 2B (2015): 213–21. http://dx.doi.org/10.30684/etj.33.2b.6.

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21

Oyebode, O. O., O. A. Oladimeji, A. Adelekun, and T. A. Akomolafe. "Performance Evaluation of Selected Classification Algorithms for Iris Recognition System." European Journal of Computer Science and Information Technology 11, no. 2 (2023): 1–12. http://dx.doi.org/10.37745/ejcsit.2013/vol11n2112.

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Quite a lot of techniques proven to be resourceful have been espoused to develop iris recognition system. Nearly hybridized, supervised and unsupervised artificial neural network techniques have been used individually in iris recognition system and other pattern recognitions but have not been compared based on some performance metrics. Counter Propagation Neural Network (CPNN) is a hybridized technique, Self-Organizing Feature Map (SOFM) is an unsupervised learning technique and Back Propagation Neural Network (BPNN) is a supervised learning technique. This research conducted a performance com

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22

Czajka, Adam, and Andrzej Pacu. "Iris Recognition System Based on Zak-Gabor Wavelet Packets." Journal of Telecommunications and Information Technology, no. 4 (June 27, 2023): 10–18. http://dx.doi.org/10.26636/jtit.2010.4.1091.

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The paper proposes a new iris coding method based on Zak-Gabor wavelet packet transform. The essential component of the iris recognition methodology design is an effective adaptation of the transformation parameters that makes the coding sensitive to the frequencies characterizing ones eye. We thus propose to calculate the between-to-within class ratio of weakly correlated Zak-Gabor transformation coefficients allowing for selection the frequencies the most suitable for iris recognition. The Zak-Gabor-based coding is non-reversible, i.e., it is impossible to reconstruct the original iris image

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23

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

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24

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

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25

C., Okedunmade Opemipo, Afolabi Adeolu O., Gbadamosi Omoniyi A., Adedeji Oluyinka T., Makinde Bukola O., and Falohun Adeleye S. "Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System." East African Scholars Journal of Engineering and Computer Sciences 7, no. 09 (2024): 35–56. http://dx.doi.org/10.36349/easjecs.2024.v07i05.001.

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An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which t

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26

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 sys

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27

Ghali, Abdulrahman Aminu, Sapiee Jamel, Kamaruddin Malik Mohamad, Nasir Abubakar Yakub, and Mustafa Mat Deris. "A Review of Iris Recognition Algorithms." JOIV : International Journal on Informatics Visualization 1, no. 4-2 (2017): 175. http://dx.doi.org/10.30630/joiv.1.4-2.62.

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With the prominent needs for security and reliable mode of identification in biometric system. Iris recognition has become reliable method for personal identification nowadays. The system has been used for years in many commercial and government applications that allow access control in places such as office, laboratory, armoury, automated teller machines (ATMs), and border control in airport. The aim of the paper is to review iris recognition algorithms. Iris recognition system consists of four main stages which are segmentation, normalization, feature extraction and matching. Based on the fi

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Mattoo, Iqra, and Parul Agarwal. "Iris Biometric Modality: A Review." Oriental journal of computer science and technology 10, no. 2 (2017): 502–6. http://dx.doi.org/10.13005/ojcst/10.02.35.

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Biometric Recognition is the most suitable and informed identification method which is used in different fields due to its uniqueness of the countless behavioural and physiological traits like hand geometry, finger prints, iris recognition, face recognition, handwriting, voice recognition, etc. Iris recognition system is widely being used as it has inherently distinctive patterns that provide a robust method for the identification purpose. Different nations have already started to use biometric recognition system for the identification purposes including patient identification, border security

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29

Kamlaskar, Chetana, and Aditya Abhyankar. "Multilinear principal component analysis for iris biometric system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1458–69. https://doi.org/10.11591/ijeecs.v23.i3.pp1458-1469.

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Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segmented to e

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30

C., Okedunmade Opemipo, Afolabi Adeolu O., Gbadamosi Omoniyi A., Adedeji Oluyinka T., Makinde Bukola O., and Falohun Adeleye S. "Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System." East African Scholars Journal of Engineering and Computer Sciences 7, no. 04 (2024): 35–56. http://dx.doi.org/10.36349/easjecs.2024.v07i04.002.

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An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which t

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31

TundeAdeniyi, Taiwo, Olatubosun Olabode, Gabriel B. Iwasokun, Samuel A. Oluwadare, and Raphael O. Akinyede. "BIOMETRIC PERSONAL IDENTIFICATION ON 2D WAVELET TRANSFORM AND CHI-SQUARED MODEL." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 9 (2015): 6074–84. http://dx.doi.org/10.24297/ijct.v14i9.3984.

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Iris recognition system consists of image acquisition, iris preprocessing, iris segmentation and feature extraction with comparism (matching) stages. The biometric based personal identification using iris requires accurate iris segmentation for successful identification or recognition. Recently, several researchers have implemented various methods for segmentation of boundaries which will require a modification of some of the existing segmentation algorithms for their proper recognition. Therefore, this research presents a 2D Wavelet Transform and Chi-squared model for iris features extraction

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32

Balasubramanian, Sruthi Kunkuma, Vijayakumar Jeganathan, and Thavamani Subramani. "Deep Learning-Based Iris Segmentation Algorithm for Effective Iris Recognition System." Proceedings of Engineering and Technology Innovation 23 (January 1, 2023): 60–70. http://dx.doi.org/10.46604/peti.2023.10002.

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In this study, a 19-layer convolutional neural network model is developed for accurate iris segmentation and is trained and validated using five publicly available iris image datasets. An integrodifferential operator is used to create labeled images for CASIA v1.0, CASIA v2.0, and PolyU Iris image datasets. The performance of the proposed model is evaluated based on accuracy, sensitivity, selectivity, precision, and F-score. The accuracy obtained for CASIA v1.0, CASIA v2.0, CASIA Iris Interval, IITD, and PolyU Iris are 0.82, 0.97, 0.9923, 0.9942, and 0.98, respectively. The result shows that t

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33

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 ex

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Anugerah Ayu, Media, and I. Komang Yogi Trisna Permana. "The discrete wavelet transform based iris recognition for eyes with non-cosmetic contact lens." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1118. http://dx.doi.org/10.11591/ijai.v12.i3.pp1118-1127.

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Iris recognition has been used as one of the biometric systems for user authentication, identification, and verification for quite some time. The basis of an iris recognition lies on the matching algorithm, which requires similarities of the iris data in the database with the captured one. In addition, nowadays using non-cosmetic or prescribed contact lenses becomes more popular and more preferred choice of many people, which makes the number of contact lens wearers significantly increases. These eyes with contact lenses add more complexity to the iris recognition process, since it can disturb

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Media, Anugerah Ayu, and Komang Yogi Trisna Permana I. "The discrete wavelet transform based iris recognition for eyes with non-cosmetic contact lens." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1118–27. https://doi.org/10.11591/ijai.v12.i3.pp1118-1127.

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Iris recognition has been used as one of the biometric systems for user authentication, identification, and verification for quite some time. The basis of an iris recognition lies on the matching algorithm, which requires similarities of the iris data in the database with the captured one. In addition, nowadays using non-cosmetic or prescribed contact lenses becomes more popular and more preferred choice of many people, which makes the number of contact lens wearers significantly increases. These eyes with contact lenses add more complexity to the iris recognition process, since it can disturb

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36

Senthil Kumar, S., S. Usha Nandhini, and G. Sangeetha. "An Enhanced Biometric System for ATM Amount Withdrawals Using Iris Biometric Recognition Method." Asian Journal of Computer Science and Technology 4, no. 2 (2015): 35–38. http://dx.doi.org/10.51983/ajcst-2015.4.2.1752.

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A biometric system provides automatic recognition of an individual based on some sort of unique feature or characteristic possessed by the individual. Biometric systems have been developed based on fingerprints, facial features, voice, hand geometry, handwriting, the retina / iris. We are living in the age, in which the demand on security is increasing greatly. Consequently, biometric recognition, which is a safe, reliable and convenient technology for personal recognition, appears. This technology makes use of physiological or behavioral characteristics to identify individual. A biometric sys

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37

Kamlaskar, Chetana, and Aditya Abhyankar. "Multilinear principal component analysis for iris biometric system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1458. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1458-1469.

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<p>Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segme

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38

Kumar, Prabhat, Manish Ahirwar, and Anjna Deen. "A Survey on Iris Recognition System." International Journal of Computer Sciences and Engineering 7, no. 7 (2019): 302–7. http://dx.doi.org/10.26438/ijcse/v7i7.302307.

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39

S.Satpute, Bhagyashri, and B. D. Jadhav. "Automated Iris Recognition System: An Overview." International Journal of Computer Applications 115, no. 17 (2015): 50–54. http://dx.doi.org/10.5120/20247-2612.

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40

Majd, BELLAAJ. "Uncertainty Theories Based Iris Recognition System." ELCVIA Electronic Letters on Computer Vision and Image Analysis 16, no. 2 (2018): 29. http://dx.doi.org/10.5565/rev/elcvia.1131.

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41

NITHESH, KUMAR R., D. RAHUL, V. DHANAKOTI, and S. SARAN. "BANK TRANSACTION USING IRIS RECOGNITION SYSTEM." i-manager’s Journal on Image Processing 8, no. 3 (2021): 15. http://dx.doi.org/10.26634/jip.8.3.18124.

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42

Chia-Te Chou, Sheng-Wen Shih, Wen-Shiung Chen, V. W. Cheng, and Duan-Yu Chen. "Non-Orthogonal View Iris Recognition System." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 3 (2010): 417–30. http://dx.doi.org/10.1109/tcsvt.2009.2035849.

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43

Prasad, Dr Puja S. "Deep Learning Based Iris Recognition System." HELIX 8, no. 4 (2018): 3567–71. http://dx.doi.org/10.29042/2018-3567-3571.

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V K, Navya. "Iris Recognition for Personal Identification System." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (2020): 169–74. http://dx.doi.org/10.22214/ijraset.2020.7031.

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Karthick, S., V. Thirumurugan, and T. Aruna. "The Survey on Iris Recognition System." International Journal of Engineering Trends and Technology 9, no. 2 (2014): 56–59. http://dx.doi.org/10.14445/22315381/ijett-v9p211.

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Seetharaman, K., and R. Ragupathy. "Iris Recognition for Personal Identification System." Procedia Engineering 38 (2012): 1531–46. http://dx.doi.org/10.1016/j.proeng.2012.06.189.

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Bansal, Atul, Ravinder Agarwal, and R. K. Sharma. "Determining diabetes using iris recognition system." International Journal of Diabetes in Developing Countries 35, no. 4 (2015): 432–38. http://dx.doi.org/10.1007/s13410-015-0296-1.

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Rana, Humayan Kabir, Md Shafiul Azam, Mst Rashida Akhtar, Julian M. W. Quinn, and Mohammad Ali Moni. "A fast iris recognition system through optimum feature extraction." PeerJ Computer Science 5 (April 8, 2019): e184. http://dx.doi.org/10.7717/peerj-cs.184.

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Abstract:

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person’s lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorpora

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Malgheet, Jasem Rahman, Noridayu Bt Manshor, and Lilly Suriani Affendey. "Iris Recognition Development Techniques: A Comprehensive Review." Complexity 2021 (August 23, 2021): 1–32. http://dx.doi.org/10.1155/2021/6641247.

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into se

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Karima BOUKARI. "Deep Learning-Based Iris Recognition System Using Unprocessed Images." Journal of Electrical Systems 20, no. 3 (2024): 2119–29. http://dx.doi.org/10.52783/jes.4010.

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The iris image is a powerful and distinctive feature in biometrics that serves as a reliable instrument for human identification. Extracting significant features is crucial for developing iris-based recognition systems. While preprocessing for iris region detection is typically the first step in such systems, it can often fail in real acquisition conditions, leading to decreased performance. This study proposes a new iris-based recognition system that directly exploits original (noisy) images for feature extraction, avoiding the bottleneck of preprocessing. Additionally, a multimodal architect

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