Abstract
Recognition for authentication using biometric features is an intricate pattern-recognizing technique. The process is extremely hard to design and build, and choosing the exact algorithms competent to fetch and extract significant features and then match them correctly, particularly in cases where the quality of the captured images is poor or low-quality image capturing devices with very small capturing areas are used. It is a false assumption that biometric recognition is a completely settled area regarding the authentication of a person just because it always gives the correct identity of an individual. Iris identification remains a very complex and intricate pattern recognition system for authenticating a person. This paper focuses on the different techniques used for authentication.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhu Y et al (2000) Biometric personal identification based on iris patterns. In: IEEE Pattern Recognition. Proceedings, vol 2, Sept 2000, pp 801–804
Wildes RP, Asmuth JC, Green GL, Hsu SC, Kolczynski RJ, Matey JR, McBride SE (1994) A system for automated iris recognition. In: Proc IEEE Workshop Mach Vis Appl pp 121–128
Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J, McBride S (1996) A machine-vision system for iris recognition. Mach Vis Appl 9:1–8
Wildes R (1997) Iris recognition: an emerging biometric technology. Proceedings of the IEEE, vol 85, no 9. September 1997
Boles W, Boashash B (1988) A human identification technique using images of the iris and wavelet transform. IEEE Trans Signal Process 46(4):1185–1188. https://doi.org/10.1109/78.668573
El-Barky HM (2001) Human iris detection using fast cooperative modular neural nets, neural networks. Proceedings of international joint conference on IJCNN ’01, vol 1, 2001. pp 577–582
Lim S, Lee K, Byeon O, Kim T (2001) Efficient iris recognition through improvement of feature vector and classifier. ETRI J, vol 23, no 2, Korea
Ma L, Tan T, Wang Y (2002) Iris recognition based on multichannel Gabor filtering. In: Proceedings of the international conference on asian conference on computer vision, pp 1–5
Lye WL, Ali C, Liau CF, Jamal AD (2002) Iris recognition using self organizing neural network. In: IEEE 2002 student conference on research and development proceedings, Shah Alam, Malaysia, pp 169–172
Sanchez-Avila C, Sanchez-Reillo R, de Martin-Roche D (2001) Iris recognition for biometric identification using dyadic wavelet transform zero-crossing. In: Proceedings of the IEEE 35th international carnahan conference on security technology, pp 272–277
Daugman J (2004) How iris recognition works. In: IEEE transactions on circuits and systems for video technology, pp 21–30
Lee J-C, Huang PS, Chiang C-S, Tu T-M, Chang C-P (2006) An empirical mode decomposition approach for iris recognition. In: Proceedings of the IEEE international conference on image processing, pp 289–292, 8–11 October, Atlanta, GA, 2006
Chen E-Y, Huang Y-P, Luo S-W (2002) An efficient iris recognition system. In: International conference on machine learning and cybernetics, pp 450–454
Cui J, Wang Y, Huang JZ, Tan T, Sun Z (2004) An iris image synthesis method based on PCA and super resolution. In: Proceedings of the 17th international conference on pattern recognition, Aug 23–26, pp 471–474. IEEE Explore Press, USA. https://doi.org/10.1109/icpr.2004.1333804
Sun Z, Tan T, Yang Y, et al (2005) Ordinal palmprint representation for personal identification. In: Proceedings of CVPR 2005, San Diego, pp 279–284
Miyazawa K, Ito K, Aoki T, Kobayashi K, Nakajima H (2005) A phase-based iris recognition algorithm. In: Zhang D, Jain AK (eds) Advances in biometrics. ICB 2006. Lecture notes in computer science, vol 3832. Springer, Berlin, Heidelberg
Lili P, Mei X (2005) The algorithm of iris image preprocessing. In: Fourth IEEE workshop on automatic identification advanced technologies (AutoID’05), 17–18 October 2005, Buffalo, New York, USA, pp 134–138
Wang J-M, Ding R-T (2005) Iris image denoising algorithm based on phase preserving. In: Sixth IEEE international conference on parallel and distributed computing, applications and technologies, PDCAT 2005, 05–08 December, 2005, Dalian, China, pp 832–835
Yuan X, Shi P (2005) Advances in biometric person authentication, Springer
Ziauddin S, Dailey MN (2009) A robust hybrid iris localization technique. In: Electrical engineering/electronics, computer, telecommunications and information technology, May
Lagree S, Bowye KW (2011) Predicting ethnicity and gender from iris texture. In: IEEE international conference on technologies for: Homeland Security
Ross A, Pasula R, Hornak L (2009) Exploring multispectral iris recognition beyond 900nm. In: IEEE 3rd international conference on biometrics: theory, applications, and systems (BTAS). Washington, DC
Lim S, Lee K, Byeon O, Kim T (2001) Efficient iris recognition through improvement of feature vector and classifier. ETRI J 23(2):61–70
Daugman J (2004) Iris recognition and anti-spoofing countermeasures. In: Seventh international biometrics conference. London
Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J, McBride S (1994) A system for automated iris recognition. In: Proceedings IEEE workshop on applications of computer vision, Sarasota, FL, pp 121–128
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prasad, P.S., Baswaraj, D. (2019). Iris Recognition Systems: A Review. In: Kumar, A., Mozar, S. (eds) ICCCE 2018. ICCCE 2018. Lecture Notes in Electrical Engineering, vol 500. Springer, Singapore. https://doi.org/10.1007/978-981-13-0212-1_54
Download citation
DOI: https://doi.org/10.1007/978-981-13-0212-1_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0211-4
Online ISBN: 978-981-13-0212-1
eBook Packages: EngineeringEngineering (R0)