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Fingerprint Classification using KEVR Algorithm

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 45 - Number 18
Year of Publication: 2012
Authors:
Sudeep D. Thepade
Dimple Parekh
Vinita Murthi
Siddharth Mantri
Bhavin Shah
10.5120/7015-9517

Sudeep D Thepade, Dimple Parekh, Vinita Murthi, Siddharth Mantri and Bhavin Shah. Article: Fingerprint Classification using KEVR Algorithm. International Journal of Computer Applications 45(18):5-7, May 2012. Full text available. BibTeX

@article{key:article,
	author = {Sudeep D. Thepade and Dimple Parekh and Vinita Murthi and Siddharth Mantri and Bhavin Shah},
	title = {Article: Fingerprint Classification using KEVR Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {45},
	number = {18},
	pages = {5-7},
	month = {May},
	note = {Full text available}
}

Abstract

Fingerprints offer an infallible means of personal identification. They are the most common and extensive form of biometric identification used at present. The use of fingerprint identification systems has become prevalent. The enormously growing size of fingerprint samples for identification systems has really become an issue these days. Fingerprint classification for the grouping of fingerprints which may further play as the pre processing of identification system has gained research momentum. The task of assigning the fingerprint to one of the considered classes is difficult. In this paper a novel technique based on Vector Quantization for fingerprint classification using Kekre's Error Vector Rotation (KEVR) is proposed. Also the comparison of the proposed method is done with the earlier presented fingerprint classification using KFCG. Here fingerprint classification is done on fingerprint images using KEVR codebook of size 8. The result obtained shows that this technique provides accuracy of 84% using KEVR codebook of size 8. Though this proposed method using KEVR takes little longer computations compared to existing method based on KFCG, it yields efficient results.

References

  • H. B. Kekre, Dr. Sudeep D. Thepade, Dimple A Parekh, "Fingerprint Classification using KFCG Algorithm", Internation Journal of Computer Sciences and Informaiton Security (IJCSIS), Vol 9, 2011.
  • Dimple Parekh, Rekha Vig, "Review of Fingerprint Classification methods based on Algorithmic Flow", Journal of Biometrics, Volume 2, Issue 1, 2011.
  • R. Cappelli, "Fast and Accurate Fingerprint Indexing Based on Ridge Orientation and Frequency", IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, , Volume: 41, Issue: 6, pp. 1511 - 1521, Dec. 2011
  • R. M. Gray, "Vector Quantization", IEEE ASSP. Mag. , pp. : 4-29, Apr. 1984.
  • Dr. H. B. Kekre, Tanuja Sarode, "New Clustering Algorithm for Vector Quantization International Journal of Computer Science and Information Security. ", 2010
  • H. B. Kekre, Sudeep D. Thepade, Tanuja K. Sarode and Vashali Suryawanshi. "Image Retrieval using Texture Features extracted from GLCM, LBG and KPE". In: International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 2010.
  • H. B. Kekre, Tanuja K. Sarode, Sudeep D. Thepade, Shrikant Sanas, "Assorted Colour Spaces to improve the Image Retrieval using VQ Codebooks Generated using LBG and KEVR", In Springer Int. Conference on Technology Systems and Management (ICTSM), 2011
  • H. B. Kekre, Tanuja K. Sarode, Sudeep D. Thepade, "Image Retrieval using Color-Texture Features from DCT on VQ Codevectors obtained by Kekre's Fast Codebook Generation" , In ICGST-International Journal on Graphics, Vision and Image Processing (GVIP), Volume 9, Issue 5, pp. : 1-8, 2009.
  • H. B. Kekre, Tanuja K. Sarode, Sudeep D. Thepade, Shrikant Sanas, "Image Retrieval using Texture Features Extracted using LBG, KPE, KFCG, KMCG, KEVR with Assorted Colour Spaces", In: International Journal of Advances in Engineering & Technology (IJAET), Vol. 2, Issue 1, pp. 520-531, Jan 2012
  • H. B. Kekre, Tanuja K. Sarode, "New Clustering Algorithm for Vector Quantization using Rotation of Error Vector", In. : International Journal of Computer Science and Information Security(IJCSIS), Vol. 7, No 3, 2011.
  • Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer design", In. : IEEE Trans. Commun. , vol. COM-28, no. 1, pp. : 84-95, 1980.
  • H. B. Kekre, Tanuja Sarode, Sudeep D. Thepade, Supriya Kamoji, "Performance Comparison of Various Pixel Window Sizes for Colorization of Greyscale images using LBG, KPE, KFCG and KEVR in Kekre's LUV Color Space"?, In: International Journal of Advances in Engineering & Technology (IJAET), Volume 1, Issue 2, December 2011
  • H. B. Kekre, Tanuja K. Sarode, "New Fast Improved Codebook Generation Algorithm for Color Images using Vector Quantization", In: International Journal of Engineering and Technology, vol. 1, No. 1, pp. :67-77, September 2008
  • Sir Edward R. Henry, "Classification and Uses of Finger Prints". London: George Rutledge & Sons, Ltd. , 1900 http://www. clpex. com/Information/Pioneers/henry-classification. pdf.
  • M. Chong, T. Ngee, L. Jun, R. Gay, "Geometric framework for fingerprint image classification", In: Pattern Recognition, volume 30, No. 9, pp. 1475-1488, 1997.
  • Ruyi Zheng, Chao Zhang, Shihua He, Pengwei Hao, "A Novel Composite Framework for Large-Scale Fingerprint Database Indexing and Fast Retrieval", Hand-Based Biometrics (ICHB), International Conference on, pp. 1 – 6, 2011
  • A. Gyaourova, A. Ross, "Index Codes for Multibiometric Pattern Retrieval", IEEE Transactions on Information Forensics and Security, On page(s): 518 - 529 Volume: 7, Issue: 2, April 2012
  • S. Biswas, N. K. Ratha, G. Aggarwal, J. Connell, "Exploring Ridge Curvature for Fingerprint Indexing", 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, (BTAS 2008), pp 1 - 6 , 2008
  • B. Bhanu, Xuejun Tan, "Fingerprint indexing based on novel features of minutiae triplets", IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume. 25, Issue. 5, pp. 616, 2003