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Palmprint Recognition Using the Combined Method of BEMD and WCB-NNSC

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13393))

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Abstract

A novel image palmprint reconstruction method using the combined method of bi-dimensional empirical mode decomposition (BEMD) and weight coding based non-negative sparse coding (WCB-NNSC) is proposed here. The BEMD algorithm is especially adaptive for non-linear and non-stationary 2D-data analysis. And the weight coding based NNSC algorithm includes more class information than that of the basic NNSC. For each original palmprint image, its first two order high frequency intrinsic mode functions (IMFs) extracted by BEMD are denoised by Wiener filter, then denosied IMFs and low frequency IMFs are fused by weighted method and normalized, moreover, using these preprocessed images as test images of WCB-NNSC, and feature basis vectors can be successfully learned Moreover, using suitable classifiers, the palmprint recognition task can be implemented. Further, in the same experimental condition, compared with palmprint feature recognition methods of standard ICA and NNSC, Simulation results show that our method proposed in this paper is indeed efficient and effective in performing palmprint recognition task.

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References

  1. Connie, T., Teoh, A., Goh, M., Ngo, D.: Palmprint recognition with PCA and ICA. Image Vis. Comput. NZ 3, 227–232 (2003)

    Google Scholar 

  2. Hoyer, P.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1427–1469 (2004)

    MathSciNet  MATH  Google Scholar 

  3. Shang, L., Zhou, Y., Sun, Z.: Image recognition using local features based NNSC model. In: The 13th International Conference on Intelligent Computing, pp. 190–199, Liverpool, UK (2017)

    Google Scholar 

  4. Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)

    Article  Google Scholar 

  5. Zhang, X.: Bingzi LYU: Gabor-2DPCA palmprint recognition based on improved BEMD. J. Xi’ Univ. Posts Telecommun. 23(2), 40–48 (2018)

    Google Scholar 

  6. Liu, Z., Peng, S.: Boundary processing of bidimensional EMD using texture synthesis. IEEE Signal Process. Lett. 12(1), 33–36 (2005)

    Article  Google Scholar 

  7. Ding, S., Du, P., Zhao, X., Zhu, Q., Xue, Y.: BEMD image fusion based on PCNN and compressed sensing. Soft. Comput. 23(20), 10045–10054 (2018). https://doi.org/10.1007/s00500-018-3560-8

    Article  Google Scholar 

  8. Chen, Y.Q., Zhang, L.N., Zhao, B.B.: Identification of the anomaly component using BEMD combined with PCA from element concentrations in the Tengchong tin belt. SW China. Geosci. Front. 10(04), 1562–1576 (2019)

    Google Scholar 

  9. An, F.P., Liu, Z.W.: Bi-dimensional empirical mode decomposition (BEMD) algorithm based on particle swarm optimization-fractal interpolation. Multimedia Tools Appl. 78(12), 17239–17264 (2019)

    Google Scholar 

  10. Ma, X., Zhou, X., An, F.: Bi-dimensional empirical mode decomposition (BEMD) and the stopping criterion based on the number and change of extreme points. J. Ambient. Intell. Humaniz. Comput. 11(2), 623–633 (2018). https://doi.org/10.1007/s12652-018-0955-4

    Article  Google Scholar 

  11. Yan, T., Zhou, C.: The research of improving PCA recognition rate of palmprints with BEMD. CAAI Trans. Intell. Syst. 8(4), 377–380 (2013)

    Google Scholar 

  12. Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognit. Artif. Intell. 13(7), 1083–1101 (1999)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the grants of National Science Foundation of China (No. 61972002).

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Authors and Affiliations

  1. Department of Communication Technology, College of Electronic Information Engineering, Suzhou Vocational University, Suzhou, 215104, Jiangsu, China

    Li Shang

  2. Department of Civil, Environmental and Geomatic Engineering, Institute of Finance and Technology, University College London, London, England

    Yuze Zhang

  3. Department of Automation, College of Electrical Engineering and Automation, Anhui University, Hefei, 230601, Anhui, China

    Zhan-li Sun

Authors
  1. Li Shang
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  2. Yuze Zhang
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  3. Zhan-li Sun
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Corresponding author

Correspondence to Li Shang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi'an Polytechnic University, Xi'an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Shang, L., Zhang, Y., Sun, Zl. (2022). Palmprint Recognition Using the Combined Method of BEMD and WCB-NNSC. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_38

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  • DOI: https://doi.org/10.1007/978-3-031-13870-6_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13869-0

  • Online ISBN: 978-3-031-13870-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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