CFP last date
22 April 2024
Reseach Article

Performance Evaluation in Iris Recognition and CBIR System based on Phase Congruency

by Pravin S. Patil, S. R. Kolhe, R. V. Patil, P. M. Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 14
Year of Publication: 2012
Authors: Pravin S. Patil, S. R. Kolhe, R. V. Patil, P. M. Patil
10.5120/7255-0258

Pravin S. Patil, S. R. Kolhe, R. V. Patil, P. M. Patil . Performance Evaluation in Iris Recognition and CBIR System based on Phase Congruency. International Journal of Computer Applications. 47, 14 ( June 2012), 13-18. DOI=10.5120/7255-0258

@article{ 10.5120/7255-0258,
author = { Pravin S. Patil, S. R. Kolhe, R. V. Patil, P. M. Patil },
title = { Performance Evaluation in Iris Recognition and CBIR System based on Phase Congruency },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 14 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number14/7255-0258/ },
doi = { 10.5120/7255-0258 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:41:50.516073+05:30
%A Pravin S. Patil
%A S. R. Kolhe
%A R. V. Patil
%A P. M. Patil
%T Performance Evaluation in Iris Recognition and CBIR System based on Phase Congruency
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 14
%P 13-18
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content based image retrieval is an important research area in image processing, with a vast domain of applications like recognition systems i. e. face, finger, iris biometric etc. It retrieves the similar type of images from repository of images based on users query. To retrieve similar images, color, and texture or shape features need to be extracted from the images and stored in the feature database. The color, texture or shape features of query image are compared with the features of images in the database. This comparison is performed using color, texture or shape distance metrics. Phase Congruency is applied to the input image for feature extraction. Advantage of the Phase Congruency method is its insensitivity to variations in image illumination and contrast. Like CBIR, the methods for iris recognition mainly focus on feature representation and matching. We use phase congruency to generate iris feature vector. Phase Congruency can be calculated using Log Gabor wavelets. The goal of this paper is to evaluate the performance of CBIR system and iris recognition that use Phase Congruency. To evaluate performance in CBIR System, precision and recall measures are used. In order to evaluate iris recognition system, recognition rate measure is used and compared with existing methods. Results of performance evaluation are discussed in paper.

References
  1. W. M. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain (2002), "Content based image retrieval at the end of early years", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp 349-1379
  2. Colin C. Venteres and Dr. Matthew Cooper (2001), "A Review of Content-Based Image Retrieval Systems", International Conference on Image Processing.
  3. R. V. Patil, K. C. Jondhale (2009)," Content Based Image Retreival Based on Phase Congruency Via Log Gabor Wavelet Filters", Proceedings of ICCVGIVP 2009. Nagpur, pp 84-85.
  4. Prakash K. S. S. , RMD Sundaram (2007)," Combining Novel Features for Content Based Image Retrieval", Sixth EURASIP Conference focused on Speech and Image Processing, 373-376.
  5. Megha Agrawal and R. P. Maheshwari (2011), " Content Based Image Retrieval Based on Log Gabor Wavelet Transform", Advaned Materials Research, Vol. 403-408 , pp. 871-878
  6. Deng Y. , B. S. Manjunath (1997), "Content-based Search of Video Using Color, Texture, and Motion", Proc. Of IEEE International Conference on Image Processing, vol. 2, pp 534-537, Santra Barbara, CA
  7. Ryszard S. Chora´s (2007), "Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics and Systems", International Journal of Biology and Biomedical Engineering, vol. 1, pp. 6-16.
  8. P. S. Patil, S. R. Kolhe, R. V. Patil, P. M. Patil (2012) ,"The Comparison of Iris Recongition using Principal Component Analysis, Log Gabor and Gabor Wavelets", International Journal Of Computer Applications, Vol-43, No. 1. , pp. 29-33
  9. B. J. Lei, Emile A. Hendriks, M. J. T. Reinders, "On Feature Extraction from Images", Technical Report, MCCWS Project, Information and Communication Theory Group, Tudeft
  10. R. V. Patil, K. C. Jondhale (2010)," Edge based technique to estimate number of clusters in k-means color image segmentation", IEEE Internatinal conference on Computer Sciene and Information Technology, Chengdu , China, vol. 2, pp. 117-121
  11. Jain, R. Bolle and S. Pankanti (1999) ," Biometrics: Personal Identification in a Networked Society", Kluwer Academic Publishers.
  12. A. Mansfield and J. Wayman (2002), "Best Practice Standards for Testing and Reporting on Biometric Device Performance," Nat'l Physical Laboratory of UK
  13. Xiaoyan Yuan, Pengfei Shi, " Iris Feature Extraction using 2 D Phase Congruency", Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05),
  14. P Kovesi. (1999), "Image features from phase congruency. ", Videre Journal of Computer Vision Research, 1(3), 1–27.
  15. P. Kovesi. (2003)," Phase congruency detects corners and edges. ",In DICTA, Sydney.
  16. M. C. Morrone, Owens (1987), "Feature Detection From Local Energy", Pattern Recognition Letters, 310-313.
  17. Zheng Liu, R. Leganneire (2006), "On the Use of Phase Congruency to evaluate Image Similarity", IEEE International Conference on Accoustics, Speech and Signal Processing, 937-940.
  18. J. G. Daugman (1993),"High confidence visual recognition of persons by a test of statistical independence", IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1148–1161.
  19. J. Daugman (1994), "Biometric Personal identification System based on iris analysis", US patent no. 529160.
  20. J. Daugman (2003), "Demodulation by Complex valued wavelets for stochastic pattern recognition", International Journal of Wavelets, Multiresolution and Information Processing, 1(1), 1-17
  21. W. W. Boles and B. Boashash (1998), "A human wavelet transform", IEEE Transactions on Signal Processing, 46(4), 1185–1188.
  22. J. Daugman (2001), "Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns," International Journal of Computer Vision, 45(1), 25-38.
  23. Hao Mang, Cuiping Xu (2006), "Iris Recognition Algorithms based on Gabor Wavelet Transforms", Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, Luoyang, China, 1785-1789
  24. Peng Fei Zhang, De-Sheng Li, Qi Wang (2004), "A Novel Iris Recognition Based On Feature Fusion", Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 3661-3665.
  25. Henning Muller, Wolfang Muller, David Squire (1999), "Performance Evaluation in CBIR: Overview and Proposals", Technical Report, University of Geneva, Switezerland , 1999
  26. L. Ma, T. Tan, Y. Wang, and D. Zhang (2003), "Personal identification based on iris texture analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1519–1533.
  27. L. Ma, T. Tan, Y. Wang, and D. Zhang (2004), "Efficient iris recognition by characterizing key local variations," IEEE Transactions on Image Processing, 13(6), 739–750.
  28. R. Johnson (1991), "Can Iris Patterns Be Used to Identify People?", Chemical and Laser Sciences Division LA-12331-PR, Los Alamos Nat'l Laboratory, Calif.
  29. L. Ma, Y. Wang, and T. Tan (2002), "Iris Recognition Based on Multichannel Gabor Filtering," Proc. Fifth Asian Conf. Computer Vision, 279-283.
  30. T. Lee (1996), "Image Representation Using 2D Gabor Wavelets," IEEE Trans. Pattern Analysis and Machine Intelligence, 18(10), 959-971.
  31. P. Belhumeur, J. Hespanha, and D. Kriegman (1997), "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7), 711-720.
  32. J. Zhang and T. Tan (2002), "Brief Review of Invariant Texture Analysis Methods," Pattern Recognition, 35(3), 735-747.
  33. V. Dorairaj and N. Schmid, and G. Fahmy (2005), "Performance Evaluation of Iris Based Recognition System Implementing PCA and ICA Techniques," in Proc. of the SPIE 2005 Symp. on Defense and Security, Conf. 5779, Orlando,.
  34. "CASIA Iris Image Database," http://www. sinobiometrics. com.
  35. D. M. Monro and D. Zhang (2005), "An Effective Human Iris Code with Low Complexity," Proc. IEEE Int'l Conf. Image Processing, 3(3), 277-280.
  36. A. K. Jain, A. Ross, and S. Prabhakar (2004), "An Introduction to Biometric Recognition," IEEE Trans. Circuits and Systems for VideoTechnology, 14, 4-20.
Index Terms

Computer Science
Information Sciences

Keywords

Phase Congruency Gabor Wavelet Log Gabor Wavelet Euclidean Distance Hamming Distance