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Fast Complex Gabor Wavelet Based Palmprint Authentication
Jyoti Malik, Ratna Dahiya, G Sainarayanan
Pages - 283 - 297     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 5   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
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KEYWORDS
Palmprint Authentication, Similarity Measurement, Sliding Window Method, Complex Gabor Wavelet
ABSTRACT
A biometric system is a pattern recognition system that recognizes a person on the basis of the physiological or behavioural characteristics that the person possesses. There is increasing interest of researchers in the development of fast and accurate personal recognition systems. In this paper, Sliding window method is used to make the system fast by reducing the matching time. The reduction in computation time indirectly reduces the overall comparison time that makes the system fast. Here, 2-D Complex Gabor Wavelet method is used to extract features from palmprint. The extracted features are stored in a feature vector and matched by hamming distance similarity measurement using sliding window approach. Reduction of 74.12% and 90.32% in comparison time is achieved using Sliding window methods. The improvement in time and accuracy as indicated by experimental results makes a system rapid and accurate.
CITED BY (4)  
1 Ayuninghemi, R., & Setyohadi, D. P. S. (2015, October). Optimum Feature for Palmprint Image Authentication. In Prosiding International conference on Information Technology and Business (ICITB) 2015 (pp. 134-138).
2 Kanchana, A., & Arumugam, S. (2013). Biometric Palm Print Recognition using Spatial Classifiers and Morphological Texture Segmentation. International Journal of Computer Applications, 64(19), 1-4.
3 Kumar, V., & Nagappan, A. Study and comparison of various point based feature extraction methods in palmprint authentication system. Editorial Committees, 82.
4 Rani, P., & Shanmugalakshmi, R. (2012). An Efficient Palmprint Recognition System Based on Extensive Feature Sets. the European Journal of Scientific Research, 71(4), 520-537.
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1 Jain A.K., Ross A., Prabhakar S.: ‘An introduction to biometric recognition’, IEEE Trans. Circuits Syst. Video Technol., 14, (1), pp. 4–20, 2004.
2 Paves?Ic´ N., Ribaric´ S., Ribaric ´ D.: ‘Personal authentication using hand-geometry and palmprint features – the state of the art’. Proc. Workshop: Biometrics – Challenges Arising from Theory to Practice, Cambridge, pp. 17–26, 2004.
3 Kumar and D. Zhang, “Combining fingerprint, palmprint and handshape for user authentication,” In Proceedings of ICPR, vol.4, pp.549- 552.
4 Kumar A., Zhang D.: ‘Personal authentication using multiple palmprint representation’, Pattern Recognit., 38, (10), pp. 1695–1704, 2005.
5 Zhang, D., Kongi, W., You, J., and Wong, M.: “Online palmprint identification”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1041–1049, 2003.
6 B.S. Manjunath, “Gabor Wavelet Transform and Application to Problems in Early vision”, IEEE, pp. 796-800.
7 Wu, X., Zhang, D., and Wang, K.: “Fisherpalms based palmprint recognition”, Pattern Recognit. Letters, vol. 24, no. 15, pp. 2829–2838, 2003.
8 Hu, D., Feng, G., and Zhou, Z.: “Two dimensional locality preserving projections with its applications to palmprint recognition”, Pattern Recognit, 40, pp. 339–402, 2007.
9 Lu, G., Zhang, D., and Wang, K.: “Palmprint recognition using eigenpalms features”, Pattern Recognit. Lett., vol. 24, no.9–10, pp. 1463–1467, 2003.
10 Liu, C.: “Gabor-based kernel PCA with fractional power polynomial models for face recognition”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 5, pp. 572–581, 2004.
11 J. Daugman, “Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression,” IEEE Trans. Acousr. Speech Signal Proc., vol. 36, no. 7, pp. 1169-1 179, 1988.
12 Sylvain Fischer and Gabriel Cristdbal, “Minimum Entropy Transform Using Gabor Wavelets For Image Compression”, IEEE, pp 428-433, 2001.
13 Jain and F. Farrokhnia, “Unsupervised texture segmentation using gabor filters. Pattern Recognition, 24(12):1167– 1186, 1991.
14 Say Song Goh, Amos Ron, Zuowei Shen “Gabor and Wavelet Frames”.
15 Muwei Jian, Haoyan GUO, Lei Liu, “Texture Image Classification Using Visual Perceptual Texture Features and Gabor Wavelet Features”, Journal Of Computers, Vol. 4, No. 8, Academy Publisher, pp 763-770,Aug. 2009.
16 The PolyU Palmprint Database: http://www4.comp. polyu.edu.hk/biometrics/
Professor Jyoti Malik
National Institute of Technology - India
jyoti_reck@yahoo.com
Dr. Ratna Dahiya
- India
Dr. G Sainarayanan
- India