International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

An Analytical Approach to IRIS-Based Biometric Authentication Systems

Abstract

Biometric systems have become integral to modern security frameworks, with iris-based systems gaining prominence due to their uniqueness, accuracy, and resistance to forgery. This paper presents a detailed analytical study of iris-based biometric systems, examining each component in the recognition pipeline, from image acquisition to feature extraction and matching. We evaluate existing techniques, assess their limitations, and propose improvements to enhance performance and reliability. A comparative analysis of segmentation and encoding methods is also provided to illustrate system optimization strategies. This work offers insights into the practical deployment and future potential of iris-based biometric systems.

Country : India

1 Deepak P. Jagtap2 Dr. Manasi R. Dixit

  1. M.E. Student, Department of Electronics and Telecommunication Engineering, K.I.T.'s College of Engineering, Gokul-Shirgaon, Kolhapur, Maharashtra, India
  2. Professor, Department of Electronics and Telecommunication Engineering, K.I.T.'s College of Engineering, Gokul-Shirgaon, Kolhapur, Maharashtra, India

IRJIET, Volume 9, Issue 8, August 2025 pp. 50-55

doi.org/10.47001/IRJIET/2025.908007

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References

  1. K.W.Bowyer, K.Hollingsworth, and P.J.Flynn, “Image Understanding for Iris Biometric: A Survey,” Computer Vision Image Understanding, IEEE Transaction, 2008, DOI:10. 1016.CVIU.2007.08.005.
  2. John Daugman and Cathryn Downing. Epigenetic randomness, complexity and singularity of human iris patterns Proceedings of the Royal Society of London- B, 268:1737–1740, 2001.
  3. John Daugman. How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):21–30, 2004.
  4. L. Flom, A. Safir, Iris recognition system, US Patent 4641394 (1987).
  5. J. Daugman, “High Confidence visual recognition of person by a test of statistical independence,” IEEE Transaction Pattern Analysis and Machine Intelligence, vol. 15, pp. 1148–1161, 1993.
  6. A.Muthukumar, C. Kasthuri and S. Kannan, “Multimodal biometric authentication using particle swarm optimization algorithm with fingerprint and iris”, ICTACT journal on image and video processing, February 2012, Vol: 02, No: 03, pp 369-374.
  7. V K Bairagi, A M Sapkal, M S Gaikwad, “The Role of Transforms in Image Compression”, Springer Journal of Institute of Engineers (India), Series B., Vol 94, No 2, June 2013. pp 135-140.
  8. Inho Choi; Daijin Kim, "Generalized Binary Pattern for Eye Detection," Signal Processing Letters, IEEE, vol.20, no.4, pp.343,346, April 2013
  9. Aditya Abhyankar, Lawrence Hornak and Stephanie Schuckers, “Biorthogonal wavelets-based iris recognition,” Proceeding of the SPIE, Biometric Technology for Human Identification II, 59; doi:10.1117/12.604212, 2005.
  10. Majumder, S.; Devi, K.J.; Sarkar, S.K., "Singular value decomposition and wavelet-based iris biometric watermarking," Biometrics, IET, vol.2, no.1, pp.21, 27, March 2013.
  11. V K Bairagi, A M Sapkal, “Selection of Wavelets for Medical Image Compression”, IEEE International conference ACT 2009, Trivendrum, India, pp 678-680, DOI: 10.1109/ACT.2009.172.
  12. A.Abhyankar and S. Schuckers, "A novel biorthogonal wavelet network system for off-angle iris recognition”, Pattern Recognition, vol. 43, pp.987 -1007 2010.
  13. Hugo Proenca, Luis A. Alexandre, Toward Covert Iris Biometrie Recognition: Experimental Results from the NICE Contests, IEEE Transactions on Information Forensics and Security, Vol. 7, No 2, Page 798-808, April 2012.
  14. V K Bairagi, A M Sapkal, "ROI based DICOM Image Compression for Telemedicine", Springer Sadhana-Academy Proceedings in Engineering Science, Vol 38, No 1,2013, pp 123-131.
  15. Chun-Wei Tan, Ajay Kumar, Unified Framework for Automated Iris Segmentation using Distantly Acquired Face Images, IEEE Transactions on Image Processing, page 4068-4079, Vol. 21, No. 09, September 2012.
  16. D. Rankin, B. Scotney, P. Morrow and B. Pierscionek "Iris recognition failure overtime: The effects of texture”, Pattern Recognition., vol. 45, pp.145 -150 2012.
  17. Faddis, K.N.; Matey, J.R.; Maxey, J.R.; Stracener, J.T., "Performance assessments of iris recognition in tactical biometric devices," Biometrics, IET, vol.2, no.3, pp.1,10, September 2013.
  18. Chun-Wei Tan; Kumar, A., "Towards Online Iris and Periocular Recognition Under Relaxed Imaging Constraints," Image Processing, IEEE Transactions on, vol.22, no.10, pp.3751-3765, Oct. 2013.
  19. Yung-Hui Li; Savvides, M., "An Automatic Iris Occlusion Estimation Method Based on High-Dimensional Density Estimation,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.35, no.4, pp.784,796, April 2013.
  20. Yao-Tung Chuang; Yu-Lun Hong; Kuo-Cheng Huang; Sheng-Wen Shih, "Autofocus of Iris Patterns Using a Triangle Aperture,” Cybernetics, IEEE Transactions on, vol.43, no.4, pp.1304,1309, Aug. 2013.
  21. K. Bowyer et al., "A Survey of Iris Biometrics Research: 2008 Update," Computer Vision and Image Understanding, 2008.
  22. S. Rakshit and D. Monro, "Human Iris Recognition Using Wavelet Transform," IEEE Trans. Pattern Analysis and Machine Intelligence, 2007.
  23. L. Masek, "Recognition of Human Iris Patterns for Biometric Identification," MSc Thesis, University of Western Australia, 2003.
  24. CASIA Iris Image Database, Institute of Automation, Chinese Academy of Sciences.
  25. Additional references to segmentation, LBP, and CNN-based iris recognition approaches.
  26. “Festvox: CMU_ARCTIC Databases.” http://www.festvox.org/cmu_arctic/ (accessed Feb. 19, 2024).

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