An investigation of IRIS Recognition Techniques: A Literature Survey

Research Article
Open access

An investigation of IRIS Recognition Techniques: A Literature Survey

C D Divya 1 , Dr. A B Rajendra 2
  • 1 Assistant Professor , VVCE, Mysuru, Karnataka, India, 570 002    
  • 2 Professor and HoD, VVCE, Mysuru, Karnataka, India, 570 002    
  • *corresponding author
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220642
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

Iris is a colored muscle located within the eye that helps to regulate the amount of light that reaches the eye. It has some specific textural details that is not easily altered or manipulated, making it a characteristic that is ideally suited to biometric systems. Iris has been considered in the field of biometric applications for authentication purpose. The main purpose of this study is, due to the stability and unique feature available in Iris region. Iris patterns play a major role in many possible identification or authentication applications due to their uniqueness, durability and stability, universality. Iris recognition techniques have evolved tremendously in biometric identification and authentication systems over the past two decades, since their evolution. The technique of Iris recognition is quantifiable, robust, and highly accurate so that it achieves the fundamental tenant of ideal biometric technology. Purpose of this paper is to include a timeline analysis of different iris recognition techniques and the development of a novel algorithm for the iris recognition system. This paper also discusses various methods used in performing the iris recognition measures involved.

Keywords:

Authentication, Unique feature., Textural details, Iris Recognition, Robust, Biometric

Divya,C.D.;Rajendra,D.A.B. (2023). An investigation of IRIS Recognition Techniques: A Literature Survey. Applied and Computational Engineering,2,855-861.
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References

[1]. J. Daugman, “How iris recognition works” IEEE Transactions on Circuits and Systems for Video Technology, (2004), vol. 14, no. 1, pp. 21– 30.

[2]. Juan Wang “An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM”, (2018) , IOP Conf. Series: Materials Science and Engineering Volume-322, Issue-5, 052030 doi:10.1088/1757-899X/322/5/052030.

[3]. Sunil S Harakannanavar, “An Efficient Algorithm for Iris Recognition”,(2018), IJANA, ISSN: 0975-0290, Volume: 09 Issue: 05 pp-3580-3587.

[4]. Alice Nithya.A. And Lakshmi, C, “Feature Extraction Techniques for Recognition of iris images.” (2016), In School of Computing, SRM University, Kancheepuram, Tamilnadu, India, IJCTA, Volume-9, Issue-28, pp. 87-92.

[5]. Maryam Soltanali Khalili, and Hamed Sadjedi, “A Robust Iris Recognition Method On Adverse Conditions”. (2013), IJCSEIT, Volume-3, Issue-5, pp-33-48,

[6]. Abhishek Verma, Chengjun Liu, and Jiancheng (Kevin) Jia, “Iris recognition based on robust iris segmentation and image enhancement”. USA (2012), IJBM, Volume-4, Issue-1, pp-56-76.

[7]. T.Rajesh,M.Karnan,and R.Sivakumar, “Performance Analysis of Iris Recognition System” ( 2014), AJCSIT, Volume-3, Issue-1, pp-01-08.

[8]. Shaaban A. Sahmoud, and Ibrahim S. Abuhaiba, “Efficient iris segmentation method in unconstrained environments”, Islamic University of Gaza, Jameaa street, Islamic University, Gaza 972, Palestine 2013.

[9]. Abduljalil Radman and Nasharuddin Zainal, “Fast and reliable iris segmentation algorithm”, Taiz University, (2013), IET Image Processing, Volume-7, Issue-1, pp-42-49.

[10]. Lee Luan Ling and Daniel Felix de Brito, “Fast and Efficient Iris Image Segmentation”, (2010), JMBE, Volume-30, Issue-6, pp 381-392.

[11]. F. Jan and I. Usman, “Iris segmentation for visible wavelength and near Infrared eye images,” (2014), Optik- Int. J. Light Electron Optics, Volume-125, Issue-16, pp- 4274–4282.

[12]. M. Frucci, M. Nappi, D. Riccio, and G. Sanniti di Baja, “WIRE: Watershed based iris recognition”, (Apr. 2016), Pattern Recognit, Volume-52, pp- 148–159.

[13]. L. Hilal , B. Daya , and P. Beauseroy , “Hough transform and Active contour for enhanced iris segmentation,” (2012), IJCSI, Volume-9, Issue- 6, pp- 1–10.

[14]. Chun-Wei Tan; Kumar, A., “Efficient and Accurate At-a-Distance Iris Recognition Using Geometric Key-Based Iris Encoding,” (Sept. 2014), Information Forensics and Security, IEEE Transactions, Volume-9, Issue-9, pp-1518,1526.

Cite this article

Divya,C.D.;Rajendra,D.A.B. (2023). An investigation of IRIS Recognition Techniques: A Literature Survey. Applied and Computational Engineering,2,855-861.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. J. Daugman, “How iris recognition works” IEEE Transactions on Circuits and Systems for Video Technology, (2004), vol. 14, no. 1, pp. 21– 30.

[2]. Juan Wang “An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM”, (2018) , IOP Conf. Series: Materials Science and Engineering Volume-322, Issue-5, 052030 doi:10.1088/1757-899X/322/5/052030.

[3]. Sunil S Harakannanavar, “An Efficient Algorithm for Iris Recognition”,(2018), IJANA, ISSN: 0975-0290, Volume: 09 Issue: 05 pp-3580-3587.

[4]. Alice Nithya.A. And Lakshmi, C, “Feature Extraction Techniques for Recognition of iris images.” (2016), In School of Computing, SRM University, Kancheepuram, Tamilnadu, India, IJCTA, Volume-9, Issue-28, pp. 87-92.

[5]. Maryam Soltanali Khalili, and Hamed Sadjedi, “A Robust Iris Recognition Method On Adverse Conditions”. (2013), IJCSEIT, Volume-3, Issue-5, pp-33-48,

[6]. Abhishek Verma, Chengjun Liu, and Jiancheng (Kevin) Jia, “Iris recognition based on robust iris segmentation and image enhancement”. USA (2012), IJBM, Volume-4, Issue-1, pp-56-76.

[7]. T.Rajesh,M.Karnan,and R.Sivakumar, “Performance Analysis of Iris Recognition System” ( 2014), AJCSIT, Volume-3, Issue-1, pp-01-08.

[8]. Shaaban A. Sahmoud, and Ibrahim S. Abuhaiba, “Efficient iris segmentation method in unconstrained environments”, Islamic University of Gaza, Jameaa street, Islamic University, Gaza 972, Palestine 2013.

[9]. Abduljalil Radman and Nasharuddin Zainal, “Fast and reliable iris segmentation algorithm”, Taiz University, (2013), IET Image Processing, Volume-7, Issue-1, pp-42-49.

[10]. Lee Luan Ling and Daniel Felix de Brito, “Fast and Efficient Iris Image Segmentation”, (2010), JMBE, Volume-30, Issue-6, pp 381-392.

[11]. F. Jan and I. Usman, “Iris segmentation for visible wavelength and near Infrared eye images,” (2014), Optik- Int. J. Light Electron Optics, Volume-125, Issue-16, pp- 4274–4282.

[12]. M. Frucci, M. Nappi, D. Riccio, and G. Sanniti di Baja, “WIRE: Watershed based iris recognition”, (Apr. 2016), Pattern Recognit, Volume-52, pp- 148–159.

[13]. L. Hilal , B. Daya , and P. Beauseroy , “Hough transform and Active contour for enhanced iris segmentation,” (2012), IJCSI, Volume-9, Issue- 6, pp- 1–10.

[14]. Chun-Wei Tan; Kumar, A., “Efficient and Accurate At-a-Distance Iris Recognition Using Geometric Key-Based Iris Encoding,” (Sept. 2014), Information Forensics and Security, IEEE Transactions, Volume-9, Issue-9, pp-1518,1526.

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