Abstract
Palmprint technology is a new branch of biometrics used to identify an individual. Palmprint has rich set of features like palm lines, wrinkles, minutiae points, texture, ridges etc. Several line and texture extraction techniques for palmprint have been extensively studied. This paper presents an intra-modal authentication system based on texture information extracted from the palmprint using the 2D- Gabor and 2D-Log Gabor filters. An individual feature vector is computed for a palmprint using the extracted texture information of each filter type. Performance of the system using two feature types is evaluated individually. Finally, we combine the two feature types using feature level fusion to develop an intra-modal palmprint recognition system. The experiments are evaluated on a standard benchmark database (PolyU Database), and the results shows that significant improvement in terms of recognition accuracy and error rates with the proposed intra-modal recognition system compared to individual representations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wu, X., Zhang, D., Wang, K.: Palm Line Extraction and Matching for Personal authentication. IEEE Transactions on Systems Man and Cybernetics Part A: Systems and Humans 36, 978–987 (2006)
Kumar, A., Wong, D.C.M., Shen, H.C., Jain, A.K.: Personal Verification using Palmprint and Hand Geometry Biometric. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 668–678. Springer, Heidelberg (2003)
Han, C.C., Cheng, H.L., Lin, C.L.: Personal authentication using palm print features. In: 5th Asian Conference on Computer Vision (ACCV), pp. 23–25 (2002)
Chin, Y.J., Ong, T.S., Goh, M.K.O., Hiew, B.Y.: Integrating Palmprint and Fingerprint for Identity Verification. In: Third International Conference on Network and System Security, pp. 437–442 (2009)
Zheng, P., Sang, N.: Using Phase and Directional Line Features for Efficient Palmprint Authentication. In: 2nd International Congress on Image and Signal Processing (CISP), pp. 1–5 (2009)
Huang, Y., Benesty, J., Chen, J.: Using the Pearson correlation coefficient to develop an optimally weighted cross relation based blind SIMO identification algorithm. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3153–3156 (2009)
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transaction on Circuit and System for Video Technology 14, 4–20 (2004)
Fan, L., Duan, H., Long, F.: Face recognition by subspace analysis of 2D Log-Gabor wavelets features. In: Third International Conference on Intelligent System and Knowledge Engineering( ISKE), pp. 1167–1172 (2008)
Zheng, P., Sang, N.: Using Phase and Directional Line Features for Efficient Palmprint Authentication. In: 2nd International Congress on Image and Signal Processing (CISP), pp. 1–5 (2009)
Wu, W.J., Xu, Y.: Correlation analysis of visual verb’s sub categorization based on Pearson’s correlation coefficient. In: International Conference on Machine Learning and Cybernetics (ICMLC), pp. 2042–2046 (2010)
PolyU Palmprint database, http://www4.comp.polyu.edu.hk/~biometrics/
Han, C., Cheng, H., Lin, C., Fan, K.: Personal authentication using palm-print features. Pattern Recognition 36, 371–381 (2003)
Funada, J., Ohta, N., Mizoguchi, M., Temma, T., Nakanishi, K., Murai, A., Sugiuchi, T., Wakabayashi, T., Yamada, Y.: Feature extraction method for palmprint considering elimination of creases. In: International Conference on Pattern Recognition, pp. 1849–1854 (1998)
Lu, G., Zhang, D., Wang, K.: Palmprint recognition using eigenpalms features. Pattern Recognition Letters 24(9-10), 1463–1467 (2003)
Wu, X., Zhang, D., Wang, K.: Fisherpalms based palmprint recognition. Pattern Recognition Letters 24, 2829–2938 (2003)
Shang, L., Huang, D.-S., Du, J.-X., Zheng, C.-H.: Palmprint recognition using fastICA algorithm and radial basis probabilistic neural network. Neurocomputing 69, 1782–1786 (2006)
Lu, G., Wang, K., Zhang, D.: Wavelet based independent component analysis for palmprint identification. In: International Conference on Machine Learning and Cybernetics, pp. 3547–3550 (2004)
Jing, X., Zhang, D.: A face and palmprint recognition approach based on discriminant DCT feature extraction. IEEE Transactions on Systems, Man, and Cybernetics-B 34, 2405–2415 (2004)
Wenxin, L., Zhang, D., Zhuoqun, X.: Palmprint identification by Fourier transform. International Journal of Pattern Recognition and Artificial Intelligence 16, 417–432 (2002)
Chen, J., Moon, Y.: Using sift features in palmprint authentication. In: International Conference on Pattern Recognition, pp. 1–4 (2008)
Badrinath, G.S., Gupta, P.: Robust biometric system using palmprint for personal verification. In: International Conference on Biometrics, pp. 554–565 (2009)
Zhang, D., Kong, A.W., You, J., Wong, M.: Online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1041–1050 (2003)
Kong, A., Zhang, D.: Competitive coding scheme for palmprint verification. In: International Conference on Pattern Recognition, pp. 520–523 (2004)
Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and oriention optimized by 2D visual cortical filters. Journal of the Optical society of America 2, 1160–1169 (1985)
Ross, A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. Springer, New York (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prasad, M.V.N.K., Kavati, I., Adinarayana, B. (2014). Palmprint Recognition Using Fusion of 2D-Gabor and 2D Log-Gabor Features. In: MartÃnez Pérez, G., Thampi, S.M., Ko, R., Shu, L. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2014. Communications in Computer and Information Science, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54525-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-54525-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54524-5
Online ISBN: 978-3-642-54525-2
eBook Packages: Computer ScienceComputer Science (R0)