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2017 | OriginalPaper | Chapter

S2DLDP with its Application to Palmprint Recognition

Authors : Ligang Liu, Jianxin Zhang, Yinghua Jiang

Published in: Image and Graphics

Publisher: Springer International Publishing

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Abstract

In this paper, we introduce the sparse two-dimensional local discriminant projections (S2DLDP) algorithm into palmprint recognition, and give an exactly recognition performance evaluation of the S2DLDP algorithm on public PolyU palmprint database. S2DLDP algorithm applies the idea of sparse for 2DLDP, possessing advantages of high computational efficiency and recognition performance. We perform the algorithm using various non-zero elements and image sizes, and then compare it with LDA, LPP and DLPP algorithm. The optimal recognition rate obtained by S2DLDP is 99.5%, which is significantly higher than the other three methods. Experiment results illuminate the excellent effectiveness of the S2DLDP algorithm for palmprint recognition.

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next chapter A Hierarchical Voting Scheme for Robust Geometric Model Fitting
Literature
1.
Tamrakar, D., Khanna, P.: Noise and rotation invariant RDF descriptor for palmprint identification. Multimedia Tools Appl. 75, 5777–5794 (2016)CrossRef
2.
Leng, L., Li, M., Kim, C., et al.: Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimedia Tools Appl. 76, 333–354 (2017)CrossRef
3.
Guo, Z.H., Zhang, D., Zhang, L., et al.: Feature band selection for online multispectral palmprint recognition. IEEE Trans. Inf. Forensics Secur. 7, 1094–1099 (2012)CrossRef
4.
Li, H., Zhang, J., Wang, L.: Robust palmprint identification based on directional representations and compressed sensing. Multimedia Tools Appl. 70, 2331–2345 (2012)CrossRef
5.
Yang, J., Zhang, D., Yang, J.Y., et al.: Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 29, 650–664 (2004)CrossRef
6.
Zhong, F., Zhang, J., Li, D.: Discriminant locality preserving projections based on L1-norm maximization. IEEE Trans. Neural Networks Learn. Syst. 25, 2065–2074 (2014)CrossRef
7.
Yang, J., Zhang, D., Frangi, A.F., et al.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004)CrossRef
8.
Li, M., Yuan, B.: 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recogn. Lett. 26, 527–532 (2005)CrossRef
9.
Hu, D., Feng, G., Zhou, Z.: Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition. Pattern Recogn. 40, 339–342 (2008)CrossRefMATH
10.
Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. J. Comput. Graph. Stat. 15, 265–286 (2006)MathSciNetCrossRef
11.
Gluhovsky, I.: Multinomial least angle regression. IEEE Trans. Neural Networks Learn. Syst. 23, 169–174 (2012)CrossRef
12.
13.
Wang, L., Cheng, H.: Robust sparse PCA via weighted elastic net. Pattern Recogn. 321, 88–95 (2012)
14.
Zhao, Q., Meng, D., Xu, Z.: Robust sparse principal component analysis. Sci. Chin. Inf. Sci. 57, 1–14 (2014)MathSciNet
15.
Lai, Z., Wan, M., Jin, Z., et al.: Sparse two-dimensional local discriminant projections for feature extraction. Neurocomputing 74, 629–637 (2011)CrossRef
16.
Wagner, A., Wright, J., Ganesh, A., et al.: Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34, 372–386 (2012)CrossRef
17.
Li, C.N., Shao, Y.H., Deng, N.Y.: Robust L1-norm two-dimensional linear discriminant analysis. Neural Netw. Official J. Int. Neural Netw. Soc. 65, 92–104 (2015)CrossRef
18.
Wang, J., Lu, C., Wang, M., et al.: Robust face recognition via adaptive sparse representation. IEEE Trans. Cybern. 44, 2368–2378 (2014)CrossRef
19.
Zhong, F., Zhang, J.: Linear discriminant analysis based on L1-norm maximization. IEEE Trans. Image Process. 22, 3018–3027 (2013)MathSciNetCrossRefMATH
Metadata
Title
S2DLDP with its Application to Palmprint Recognition
Authors
Ligang Liu
Jianxin Zhang
Yinghua Jiang
Copyright Year
2017
Book
Image and Graphics

Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71607-7_1

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