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Relevant bibliographies by topics / Iris recognition system

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Academic literature on the topic 'Iris recognition system'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Iris recognition system"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Iris recognition system"

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Hasegawa, Robert Shigehisa. "Using synthetic images to improve iris biometric performance." Scholarly Commons, 2012. https://scholarlycommons.pacific.edu/uop_etds/827.

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Nassar, Alaa S. N. "A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16917.

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Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal

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Mohammadi, Arvacheh Ehsan. "A Study of Segmentation and Normalization for Iris Recognition Systems." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2846.

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Iris recognition systems capture an image from an individual's eye. The iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented or normalized properly. This thesis is to enhance the performance of segmentation and normalization processes in iris recognition systems to increase the overall accuracy. <br /><br /> The prev

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Fouad, Marwa. "Towards Template Security for Iris-based Biometric Systems." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22736.

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Personal identity refers to a set of attributes (e.g., name, social insurance number, etc.) that are associated with a person. Identity management is the process of creating, maintaining and destroying identities of individuals in a population. Biometric technologies are technologies developed to use statistical analysis of an individual’s biological or behavioral traits to determine his identity. Biometrics based authentication systems offer a reliable solution for identity management, because of their uniqueness, relative stability over time and security (among other reasons). Public accepta

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Langgartner, Florian S. [Verfasser], and Elmar [Akademischer Betreuer] Lang. "Machine Learning Techniques and Optical Systems for Iris Recognition from Distant Viewpoints / Florian S. Langgartner ; Betreuer: Elmar Lang." Regensburg : Universitätsbibliothek Regensburg, 2020. http://d-nb.info/1214886892/34.

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JAIN, AKSHAT. "IRIS RECOGNITION SYSTEM." Thesis, 2014. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15609.

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Iris Recognition is one of the most important biometric recognition systems to authenticate people. Iris recognition works by matching the iris patterns of two individuals. In this paper an efficient iris recognition system is proposed. The process used herein concentrates on image segmentation, normalization, feature extraction and encoding, and verification. Segmentation is done with the help of a great mix of Integro-Differential operator and Hough transformation. This approach provides you with better noise reduction and better iris deduction. Normalization of iris region is performe

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Li, Yue-Sheng, and 李岳陞. "An iris recognition system." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/22632660094355856320.

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碩士<br>淡江大學<br>資訊工程學系碩士班<br>100<br>Iris recognition system uses iris for distinguishing the identity of person using rich iris texture feature. To effectively remove noise and precisely segment stable iris region is a crucial stage prior to recognition. Most noises on iris images are caused by occlusion of eyelids or eyelashes in some certain areas. In this paper, we proposed an iris recognition system, which precisely locates and segments iris regions. We extract iris feature from a relatively reliable portion of the iris region using the DoG filter. Experimental results show that the proposed

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Wu, Ting-Rong, and 吳庭榕. "A Multi-View Iris Recognition System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/91218619191404791690.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>93<br>Recently, techniques for personal identification with biometrics have drawn more and more attention from both the academic community and the industry. In view of the recognition accuracy and the user friendliness, iris recognition techniques have stood out among the biometric personal identification techniques. In this thesis, we will discuss in depth the personal identification technique with iris images taken at different angles of views. In general, an iris identification system includes four modules for iris image acquisition, iris localization, iris featu

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Hung, Cheng-kai, and 洪晟凱. "A Design of Iris Recognition System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/28348701416153908184.

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碩士<br>國立中山大學<br>電機工程學系研究所<br>104<br>Conventional identification tools, such as keys, magnetic cards and secret codes, can be easily duplicated and cracked. Missing keys, losing cards and forgetting passwords are quite usual that result in inconvenience and insecurity in our daily life and work. In the recent years, biometrics technology is applied in the authentication industry due to its unique, stable and non-counterfeitable features. Face, iris, retina, palm print, fingerprint and voiceprint are the common biometric features. However, first of all, according to the statistical analysis

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CHIANG, YI-CHANG, and 蔣宜璋. "Implementation of An Iris Recognition System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/00103121457133322622.

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碩士<br>國立暨南國際大學<br>通訊工程研究所<br>98<br>In general daily life, the methods that we use for identity are the traditional and simple way for the majority. With breakthrough technology and progress, people demand more of the information security. The traditional methods of identification became insufficient and inconvenient so the present biometric identity recognition technology is paid more attention. In a variety of biometric technology, the iris image has more unique features which are more stable for the effect in the recognition system. Therefore, the iris recognition system will be discussed

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Books on the topic "Iris recognition system"

1

Bodade, Rajesh M. Iris analysis for biometric recognition systems. Springer, 2014.

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Bodade, Rajesh M., and Sanjay N. Talbar. Iris Analysis for Biometric Recognition Systems. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1853-1.

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Burge, Mark J. Handbook of Iris Recognition. Springer London, 2013.

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Rathgeb, Christian. Iris Biometrics: From Segmentation to Template Security. Springer New York, 2013.

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IEEE/RSJ International Conference on Intelligent Robots and Systems (2001 Maui, Hawaii). Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2001): Expanding the societal role of robotics in the next millennium, October 29-November 3, 2001, Outrigger Wailea Resort, Maui, Hawaii, USA. IEEE, 2001.

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Talbar, Sanjay N., and Rajesh M. Bodade. Iris Analysis for Biometric Recognition Systems. Springer London, Limited, 2014.

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Bowyer, Kevin W., and Mark J. Burge. Handbook of Iris Recognition. Springer, 2018.

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Bowyer, Kevin W., and Mark J. Burge. Handbook of Iris Recognition. Springer London, Limited, 2016.

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Burge, Mark J., and Kevin Bowyer. Handbook of Iris Recognition. Springer, 2015.

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Bowyer, Kevin W., and Mark J. Burge. Handbook of Iris Recognition. Springer, 2016.

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Book chapters on the topic "Iris recognition system"

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Liu, Yong, Yuqing He, Chunquan Gan, Jiadan Zhu, and Li Li. "A Review of Advances in Iris Image Acquisition System." In Biometric Recognition. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_26.

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Khanam, Ruqaiya, Zohreen Haseen, Nighat Rahman, and Jugendra Singh. "Performance Analysis of Iris Recognition System." In Data and Communication Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2254-9_14.

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Colores, Juan M., Mireya García-Vázquez, Alejandro Ramírez-Acosta, and Héctor Pérez-Meana. "Iris Image Evaluation for Non-cooperative Biometric Iris Recognition System." In Advances in Soft Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25330-0_44.

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Nigam, Aditya, Lovish, Amit Bendale, and Phalguni Gupta. "Efficient Iris Recognition System Using Relational Measures." In Computational Forensics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20125-2_6.

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Mohammed, Nada Fadhil, Suhad A. Ali, and Majid Jabbar Jawad. "Iris Recognition System Based on Lifting Wavelet." In Cognitive Informatics and Soft Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1451-7_27.

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Radu, Petru, Konstantinos Sirlantzis, Gareth Howells, Sanaul Hoque, and Farzin Deravi. "A Multi-algorithmic Colour Iris Recognition System." In Soft Computing Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33941-7_7.

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Gan, Chunquan, Yuqing He, Jiaqi Li, Huiying Ren, and Jixing Wang. "An Embedded Self-adaptive Iris Image Acquisition System in a Large Working Volume." In Biometric Recognition. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_45.

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Nseaf, Asama Kuder, Azizah Jaafar, Haroon Rashid, Riza Sulaiman, and Rahmita Wirza O. K. Rahmat. "Design Method of Video Based Iris Recognition System (V-IRS)." In Advances in Visual Informatics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02958-0_48.

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Gu, Hongying, Yueting Zhuang, Yunhe Pan, and Bo Chen. "A New Iris Recognition Approach for Embedded System." In Embedded Software and Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11535409_14.

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Lee, Yooyoung, P. Jonathon Phillips, and Ross J. Micheals. "An Automated Video-Based System for Iris Recognition." In Advances in Biometrics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01793-3_117.

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Conference papers on the topic "Iris recognition system"

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Borkar, Kavita, and Suresh Salankar. "Expression of Concern for: IRIS Recognition System." In 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2021. http://dx.doi.org/10.1109/icmnwc52512.2021.10702983.

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Srikanth, J., Madamchatti Sai Chetana, Jarupla Tarachand, Varthyavath Hanumanth, and Punna Sagar. "Smart Voting System Utilizing Iris Recognition Technology." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00021.

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Bharathi, R., and M. B. Anandaraju. "Iris Recognition System Using Circular Hough Transform and HOG." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10895459.

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Borkar, Kavita, and Suresh Salankar. "IRIS Recognition System." In 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2021. http://dx.doi.org/10.1109/icmnwc52512.2021.9688382.

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Desoky, Aly I., Hesham A. Ali, and Nahla B. Abdel-Hamid. "Enhancing iris recognition system performance." In Systems (ICCES). IEEE, 2010. http://dx.doi.org/10.1109/icces.2010.5674902.

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Wheeler, Frederick W., A. G. Amitha Perera, Gil Abramovich, Bing Yu, and Peter H. Tu. "Stand-off Iris Recognition System." In 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems. IEEE, 2008. http://dx.doi.org/10.1109/btas.2008.4699381.

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"An Effective Iris Recognition System." In International Conference on Advances in Engineering and Technology. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0314050.

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Shruthi, A., and A. Sindhuja. "Design of Iris Recognition System." In Second International Conference on Signal Processing, Image Processing and VLSI. Research Publishing Services, 2015. http://dx.doi.org/10.3850/978-981-09-6200-5_57.

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Jhamb, Mansi, and Vinod Kumar Khera. "Iris based human recognition system." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705766.

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Mehrotra, Hunny, Phalguni Gupta, and Anil K. Kaushik. "Texture based iris recognition system." In SPIE Defense and Security Symposium, edited by Harold H. Szu and F. Jack Agee. SPIE, 2008. http://dx.doi.org/10.1117/12.784452.

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Reports on the topic "Iris recognition system"

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these

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