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Abstract
The performance of any biometric system depends on the reliable and robust feature extraction.  Biometric recognition refers to the use of distinctive anatomical (e.g., fingerprints, face, iris, palm) and behavioral (e.g., speech) characteristics for automatically recognizing individuals. Iris recognition was found to be most accurate bio-metrics technology. Apart from the red, green, and blue (RGB) format, we analyze significance of using HSV, Otsu’s multi thresholding is applied on V channel, K-means clustering is applied to merge over segmented region to get sclera mask. Pyramidal directional filtering approach (Contourlets) for feature extraction for ocular biometrics are proposed. In this paper, we pursue a “true†two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. Our approach starts with a discrete domain construction. For Classification, linear discriminant analysis (LDA) is used.
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How to Cite
Jadhav, N. S., & Dharwadkar, P. S. N. (2015). Biometric Identification Based on Conjuctival Vasculature Pattern using Contourlet Transform. International Journal of Emerging Trends in Science and Technology, 2(07). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/784
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2. D. Bhattacharyya, R. Ranjan , P. Das, K. Tai-hoon, and S. K. Bandyopdhyay, "Biometric Authentication Techniques and its Future Possibilities," in Second International Conference on Computer and Electrical Engineering, 2009. ICCEE '09, 2009, pp. 652-655.
3. L. Nadel and T. Cushing, "Eval-Ware: Biometrics Resources [Best of the Web]," IEEE Signal Processing Magazine , vol. 24, pp. 136-139, 2007.
4. A. K. Jain, A. Ross, and S. Prabhakar, "An introduction to biometric recognition," IEEE Transactions on Circuits and Systems for Video Technology, , vol. 14, pp. 4-20, 2004.
5. K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, "Image understanding for iris biometrics: A survey," Computer Vision and Image Understanding, vol. 110, pp. 281-307, 2008.
6. J. Daugman, "New Methods in Iris Recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, , vol. 37, pp. 1167-1175, 2007.
7. V. Gottemukkula, S. K. Saripalle, S. P. Tankasala, R. Derakhshani, R. Pasula, and A. Ross, "Fusing iris and conjunctival vasculature: Ocular biometrics in the visible spectrum," in IEEE Conference on Technologies for Homeland Security (HST), 2012, 2012, pp. 150-155.
8. R. Derakhshani, A. Ross and S. Crihalmeanu, "A New Biometric Modality Based On Conjunctival Vasculature," Proc. of Artificial Neural Networks in Engineering (ANNIE), (St. Louis, USA), November 2006.
9. S. P. Tankasala, P. Doynov, R. R. Derakhshani, A. Ross, and S. Crihalmeanu, "Biometric recognition of conjunctival vasculature using GLCM features," in International Conference on Image Information Processing (ICIIP), 2011, 2011, pp. 1-6.
10. M. Tistarelli, M. Nixon, S. Crihalmeanu, A. Ross, and R. Derakhshani, "Enhancement and Registration Schemes for Matching Conjunctival Vasculature," in Advances in Biometrics. vol. 5558, ed: Springer Berlin / Heidelberg, 2009, pp. 1240-1249.
11. O. Kangrok and T. Kar-Ann, " Extracting sclera features for cancelableidentity verification," in International Conference on Biometrics (ICB), 2012 5th IAPR, 2012, pp. 245-250.
12. N. L. Thomas, Y. Du, and Z. Zhou, "A new approach for sclera vein recognition," pp. 770805-770805, 2010.
13. Z. Zhou, Y. Du, N. L. Thomas, and E. J. Delp, "Multimodal eye recognition," pp. 770806-770806, 2010.
14. Vijay jump, Mandar Sohani, “Color Image Segmentation Using K-means Clustering and Otsu’s Adaptive thresholdingâ€, IJITEE, 2014.
15. M. N. Do and M. Vetterli, "The contourlet transform: an efficient directional multiresolution image representation," IEEE Transactions on Image Processing, , vol. 14, pp. 2091-2106, 2005.
16. M. N. Do and M. Vetterli, "Framing pyramids," IEEE Transactions on Signal Processing, , vol. 51, pp. 2329-2342, 2003.
17. A. Azizi and H. Pourreza, "A Novel Method Using Contourlet to Extract Features for Iris Recognition System," in Emerging Intelligent Computing Technology and Applications. vol. 5754, Springer Berlin Heidelberg, 2009, pp. 544-554.
18. P. J. Burt and E. H. Adelson, "The Laplacian Pyramid as a Compact Image Code," IEEE Transactions on Communications, vol. 31, pp. 532-540, 1983.
19. M. Vetterli, "Multi-dimensional sub-band coding: Some theory and algorithms," Signal Processing, vol. 6, pp. 97-112, 1984.
20. Zhihua Qiao,Lan Zhou and Jianhua Z. Huang, “Effective Linear Discriminant Analysis for High Dimensional, Low Sample Size Dataâ€,2008.
21. "Hugo Proença, SÃlvio Filipe, Ricardo Santos, João Oliveira, LuÃs A. Alexandre; The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-The-Move and At-A-Distance, IEEE Transactions on Pattern Analysis and Machine Intelligence, August, 2010, volume 32, number 8, pag. 1529-1535, ISSN: 0162-8828".