We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Biometric Quality: Analysis of Iris Recognition Techniques with other Biometric Authentication Systems

by Prashant Kapoor, Paresh Rawat
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 4
Year of Publication: 2017
Authors: Prashant Kapoor, Paresh Rawat
10.5120/ijca2017913280

Prashant Kapoor, Paresh Rawat . Biometric Quality: Analysis of Iris Recognition Techniques with other Biometric Authentication Systems. International Journal of Computer Applications. 162, 4 ( Mar 2017), 31-36. DOI=10.5120/ijca2017913280

@article{ 10.5120/ijca2017913280,
author = { Prashant Kapoor, Paresh Rawat },
title = { Biometric Quality: Analysis of Iris Recognition Techniques with other Biometric Authentication Systems },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 4 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number4/27234-2017913280/ },
doi = { 10.5120/ijca2017913280 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:06.690923+05:30
%A Prashant Kapoor
%A Paresh Rawat
%T Biometric Quality: Analysis of Iris Recognition Techniques with other Biometric Authentication Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 4
%P 31-36
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A wide variety of systems require reliable personal recognition schemes to either conform or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that only a legitimate user, access the rendered service. In this paper, many Biometric authentications will be studied and conclude that Iris biometric is effective, fast and reliable for the person recognition compare to any other well-known biometrics. This paper presents a survey of different concepts and interpretations of biometric quality. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes [1] are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in iris, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images [2], to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems.

References
  1. A. K. Jain, A. Ross, and S. Pankanti, "Biometrics: A Tool for Information Security", IEEE Transactions on Information Forensics and Security, Vol. 1, No. 2, 2006, pp.125-143.
  2. J. Daugman, "New Methods in Iris Recognition", IEEE Trans. on Systems, Man, and Cybernetics, Vol. 37, No. 5, 2007, pp. 1167-1175.
  3. R. Wildes, "Iris Recognition: an Emerging Biometric Technology", Proceedings of the IEEE, Vol. 85, No. 9, 1997, pp. 1348-1363.
  4. W. Boles, and B. Boashash, "A Human Identification Technique Using Images of the Iris and Wavelet Transform", IEEE Trans. on Signal Processing, Vol. 46, No. 4, 1998, pp. 1185-1188.
  5. W. Kong, and D. Zhang, "Accurate Iris Segmentation Based on Novel Reflection and Eyelash Detection Model", in International Symposium on Intelligent Multimedia, Video and Speech Processing, 2001, pp. 263-266.
  6. L. Ma, and T. Tisse, "Personal Recognition Based on Iris Texture Analysis", IEEE Trans. on PAMI, Vol. 25, No. 12, 2003, pp. 1519-1533.
  7. N. Schmid, M. Ketkar, H. Singh and B. Cukic, "Performance Analysis of Iris Based Identification System the Matching Scores Level", IEEE Transactions on Information Forensics and Security, Vol. 1, No. 2, 2006, pp. 154-168.
  8. Said, Amir; Pearlman, William A. (June 2015). "A new fast and efficient image codec based on set partitioning in hierarchical trees". IEEE Transactions on Circuits and Systems for Video Technology 6 (3): 243–250. doi:10.1109/76.499834. ISSN 1051-8215. http://reference.kfupm.edu.sa/content/n/e/a_new_fast_and_e_cient_image_codec_based_661859.pdf
  9. MayankVatsa , Richa Singh, P .Gupta, Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing
  10. A.K.Jain,A.Ross, and S.Pankanti,"Biometrics: ATool for Information Security", IEEE Transactions on InformationForensicsandSecurity,Vol.1,No.2,2006,pp. 125-143.
  11. V.Dorairaj,A. Schmid, and G. Fahmy,"Performance Evaluation of Iris Based Recognition System Implementing PCA and ICA Encoding Techniques", in Proceedings of SPIE,2005,pp.51-58.
  12. S.Shah,andA.Ross,"Iris Segmentation Using Geodesic Active Contours", IEEE Trans. on Information Forensics andSecurity, Vol.4,No.4,2009,pp.824-836.
  13. I.JShaikh, S.M Mukane, The Effect of Iris Image Compression on Recognition Performance published in International Journal of Computer Applications (0975 – 8887) Volume 113 – No. 18, March 2015
  14. A.P. Bradley and F.W.M. Stentiford, “JPEG2000 and region of interest coding,” Digital Imaging Computing Techniques and Applications, Melbourne, 2002. Available at: http://www.ee.ucl.ac.uk/fstentif/DICTA02.pdf
  15. Surjeet Singh, Kulbir Singh, Segmentation Techniques for Iris Recognition System, International Journal of Scientific & Engineering Res V volume 2, Issue 4, April
  16. J. Daugman. How iris recognition works. Proceedings of nternational Conference on Image Processing, Vol. 1, 2002.
  17. A.M.Raid, W.M.Khedr, M. A. El-dosuky and Wesam Ahmed, Jpeg Image Compression Using Discrete CosineTransform.pdf - A Survey, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.2, April 2014
  18. Mahmud Hasan, Kamruddin Md. Nur, An Improved JPEG Image Compression Technique based on Selective Quantization.pdf, International Journal of Computer Applications (0975 - 8887) Volume 55 - No. 03, October 2014
  19. Brian C. Smith, Lawrence A. Rowe, Compressed Domain Processing of JPEG-encoded images. Pdf , International Journal of Computer Applications (0975 - 8887) Volume 65 - No. 04, September 2015
  20. Majid Rabbani, Rajan Joshi, An overview of the JPEG2000 Image Communication 17 (2002) 3–48,Elsevier volume 48, 2014
Index Terms

Computer Science
Information Sciences

Keywords

Biometric Segmentation Normalization Iris Tissue Encoding and Matching

Powered by PhDFocusTM