Abstract
In the field of palmprint recognition, traditional classification methods such as PCA and LDA only exploit training samples to build the model of classifiers while ignoring the feedback of test sample during the process of recognition. In this paper, both types of samples will be taken into account to make an optimal representation of the test sample. We first exploit nearest neighbor principle to construct the initial optimal training sample set for representation. This optimal set will be updated by adding a training sample which works well with current optimal set to obtain the least representation error to the test sample. The sample selection step does not stop until we acquire the optimal set with sufficient number of training samples for classification. Comparative experiments have been conducted on PolyU multispectral palmprint database which validates the proposed method. Moreover, a study on parameter points out that a small number of selected training samples is good for recognition in blue and green channels while much more selected training samples are suitable for improving recognition in red channel.
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
Zhang, D., Kong, W.K., You, J., Wong, M.: Online palmprint identification. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 1041–1050 (2003)
Kong, A.W.K., Zhang, D.: Competitive coding scheme for palmprint verification. In: Proc. 17th Int. Conf. on Pattern Recognition, pp. 520–523. IEEE Press, Piscataway (2004)
Lu, G., Zhang, D., Wang, K.: Palmprint recognition using eigenpalms features. Pattern Recognition Letters 24, 1463–1467 (2003)
Wu, X., Wang, K., Zhang, D.: Palmprint recognition using Fisher’s linear discriminant. In: Proc. Int. Conf. on Machine Learning and Cybernetics, pp. 3150–3154. IEEE Press, Piscataway (2003)
Sang, H., Yuan, W., Zhang, Z.: Research of Palmprint Recognition Based on 2DPCA. In: Yu, W., He, H., Zhang, N. (eds.) ISNN 2009, Part II. LNCS, vol. 5552, pp. 831–838. Springer, Heidelberg (2009)
Ma, Y., Sun, J.: Palmprint Recognition based on 2DPCA-moment invariant. In: Proc. 5th Int. Conf. on Image and Graphics, pp. 149–155. IEEE Press, Piscataway (2009)
Du, F., Yu, P., Li, H., Zhu, L.: Palmprint Recognition Using Gabor Feature-Based Bidirectional 2DLDA. In: Yu, Y., Yu, Z., Zhao, J. (eds.) CSEEE 2011, Part II. CCIS, vol. 159, pp. 230–235. Springer, Heidelberg (2011)
Xu, Y., Zhang, D., Yang, J., Yang, J.Y.: A two-phase test sample sparse representation method for use with face recognition. IEEE Trans. on Circuits and Systems for Video Technology 21, 1255–1262 (2011)
Xu, Y., Zuo, W., Fan, Z.: Supervised sparse representation method with a heuristic strategy and face recognition experiments. Neurocomputing 79, 125–131 (2012)
Golub, G.H., Van Loan, C.F.: Matrix computations. The Johns Hopkins University Press, Maltimore (1996)
PolyU multispectral palmprint Database, http://www.comp.polyu.edu.hk/~biometrics/MultispectralPalmprint/MSP.htm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wen, J., Cui, J., Lai, Z., Mi, J. (2013). A Competitive Sample Selection Method for Palmprint Recognition. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-36669-7_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
eBook Packages: Computer ScienceComputer Science (R0)