Abstract
Biometrics are vital in security. Facial recognition, fingerprints, and iris recognition are all examples of computer vision biometrics. Unique authentication based on iris structure is one of the finest approaches for iris identification. This research provides an iris-based biometric identification system combining CNN and Softmax classifier. The system consists of picture augmentation by histogram equalization, image reduction by discrete wavelet transformation (DWT), segmentation by circular Hough transform and canny edge detector, and normalizing by Daugman's rubber-sheet model. Each picture is adjusted before being fed into the DenseNet201 model. The Softmax classifier then sorts the 224 IITD iris classes into 249 CASIA-Iris-Interval classes, 241 UBIRIS.v1 iris classes, and 898 CASIA-Iris-Thousand classes. The performance of our suggested system is determined by the setting of its deep networks and optimizers. In terms of accuracy, it exceeds existing approaches by 99%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Thomas T, George A, Devi KI (2016) Effective iris recognition system. Proc Technol 25:464–472
Al-Waisy AS, Qahwaji R, Ipson S, Al-Fahdawi S, Nagem TA (2018) A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal Appl 21(3):783–802
Liu M, Zhou Z, Shang P, Xu D (2019) Fuzzified image enhancement for deep learning in iris recognition. IEEE Trans Fuzzy Syst 28(1):92–99
Sujana S, Reddy VSK (2021) An effective CNN based feature extraction approach for iris recognition system. Turkish J Comput Math Educ (TURCOMAT) 12(6):4595–4604
Alaslani MG, Elrefaei LA (2019) Transfer lerning with convolutional neural networks for iris recognition. Int J Artif Intell Appl 10(5):47–64
CASIA Iris Image Database Version 4.0 (CASIA-Iris-Thousand). Available from: http://biometrics.idealtest.org/dbDetailForUser.do?id=4
CASIA Iris Image Database Version 4.0 (CASIA-Iris-Interval). Available from: http://biometrics.idealtest.org/dbDetailForUser.do?id=4
IIT Delhi Database. Available from: http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm
UBIRIS Image Database Version 1.0. Available from: http://iris.di.ubi.pt/ubiris1.html
Sharma VP, Mishra SK, Dubey D (2013) Improved iris recognition system using wavelet transforma and ant colony optimization. In: 5th International conference on computational intelligence and communication networks (CICN). IEEE, New York, pp 243–246
Jyoti P, Parvati B, Sandeep KG, Shubh Lakshmi A (2016) New improved feature extraction approach of iris recognition. Int J Comput Syst 3:1–3
Ma L, Tan T, Wang Y, Zhang D (2004) Efficient iris recognition by characterizing key local variation. IEEE Trans Image Process 13:739–750
Verma P, Dubey M, Basu S, Verma P (2012) Hough transform method for iris recognition-a biometric approach. Int J Eng Innov Technol (IJEIT) p 1
Verma P, Dubey M, Verma P, Basu S (2012) “daughman”s algorithm method for iris Recognition-a biometric approach. Int J Emerg Technol Adv Eng p 2
Daugman JG (1993) High condence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15:1148–1161
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overftting. J Mach Learn Res 15:1929–1958. ISSN: 1532-4435
Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE Conference on computer vision and pattern recognition (CVPR). Honolulu, pp 2261–2269
Al-Waisy AS et al (2017) A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal Appl pp 1–20
Syafeeza A et al (2015) Convolutional neural networks with fused layers applied to face recognition. Int J Comput Intell Appl 14(03):1550014
Abdulreda A, Obaid A (2022) A landscape view of deepfake techniques and detection methods. Int J Nonlinear Anal Appl 13(1):745–755. https://doi.org/10.22075/ijnaa.2022.5580
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Arnoos, S.M., Sahan, A.M., Ansaf, A.H.O., Al-Itbi, A.S. (2023). An Intelligent Iris Recognition Technique. In: Kumar, R., Pattnaik, P.K., R. S. Tavares, J.M. (eds) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol 445. Springer, Singapore. https://doi.org/10.1007/978-981-19-1412-6_17
Download citation
DOI: https://doi.org/10.1007/978-981-19-1412-6_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1411-9
Online ISBN: 978-981-19-1412-6
eBook Packages: EngineeringEngineering (R0)