Abstract
Palmprint authentication is becoming one of the most important biometric techniques because of its high accuracy and ease to use. The features on palm, including the palm lines, ridges and textures, etc., are resulted from the gray scale variance of the palmprint images. This paper characterizes these variance using different order differential operations. To avoid the effect of the illumination variance, only the signs of the pixel values of the differential images are used to encode palmprint to form palmprint differential code (PDC). In matching stage, normalized Hamming distance is employed to measure the similarity between different PDCs. The experimental results demonstrate that the proposed approach outperforms the existing palmprint authentication algorithms in terms of the accuracy, speed and storage requirement and the differential operations may be considered as one of the standard methods for palmprint feature extraction.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhang, D.: Automated Biometrics–Technologies and Systems. Kluwer Academic Publishers, Dordrecht (2000)
Jain, A., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society. Kluwer Academic Publishers, Dordrecht (1999)
Jain, A., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)
Han, C., Chen, H., Lin, C., Fan, K.: Personal authentication using palm-print features. Pattern Recognition 36(2), 371–381 (2003)
Kumar, A., Wong, D., Shen, H., Jain, A.: 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)
Zhang, D., Kong, W., You, J., Wong, M.: Online palmprint identification. IEEE Trans. Pattern Anal. Machine Intell. 25(9), 1041–1050 (2003)
Kong, A.W.-K., Zhang, D.: Feature-level fusion for effective palmprint authentication. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 761–767. Springer, Heidelberg (2004)
Wu, X., Zhang, D., Wang, K.: Palm-line extraction and matching for personal authentication. IEEE Trans. Syst., Man, Cybern. A 36(5), 978–987 (2006)
Jiaa, W., Huanga, D.S., Zhang, D.: Palmprint verification based on robust line orientation code. Pattern Recognition 41(5), 1504–1513 (2008)
Kong, A., Zhang, D.: Competitive coding scheme for palmprint verification. In: IEEE International Conference on Pattern Recognition, pp. 520–523 (2004)
Sun, Z., Tan, T., Wang, Y., Li, S.Z.: Ordinal palmprint represention for personal identification. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 (2005)
PolyU Palmprint Database, http://www.comp.polyu.edu.hk/~biometrics/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, X., Wang, K., Xu, Y., Zhang, D. (2009). Differential Feature Analysis for Palmprint Authentication. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-03767-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
eBook Packages: Computer ScienceComputer Science (R0)