Palmprint Recognition Using Data Field and PCNN

  • Yanxia Wang
  • Jianmin Zhao
  • Guanghua Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

In this paper, an approach is proposed for palmprint recognition, which uses PCNN and data field theory to extract local statistical structure features of a palmprint. In the method, the data field theory is firstly introduced to obtain a relative palmprint data field, which enhances the palm line information. Then the relative data field is input into a PCNN. Next, the local statistical structure features with four values are extracted from each sub-region. At last, all of local statistic-structural feature vectors are weighted and combined into a long feature vector to represent the palmprint. Experiments show that the novel features can effectively characterize different palmprints.

Keywords

Palmprint recognition data field PCNN feature extraction 

References

  1. 1.
    Zhang, D., Wai-Kin, K., You, J., Wong, M.: Online Palmprint Identification. Pattern Analysis and Machine Intelligence 25(9), 1041–1050 (2003)CrossRefGoogle Scholar
  2. 2.
    Yue, F., Zuo, W.M., Zhang, D.: Survey of palmprint recognition algorithms. Acta Automatica Sinica 36(3), 353–365 (2010)CrossRefGoogle Scholar
  3. 3.
    Leung, F.L.M., Yu, X.: Palmprint matching using line features. In: Proceedings of the 8th International Conference on Advanced Communication Technology, Gangwon-Do, Korea, pp. 1577–1582 (2006)Google Scholar
  4. 4.
    Duta, N., Jain, A.K., Mardia, K.V.: Matching of palmprints. Pattern Recognition Letters 23(4), 477–485 (2007)CrossRefGoogle Scholar
  5. 5.
    Kong, W., Zhang, D., Li, W.: Palmprint feature extraction using 2-D Gabor filters. Pattern Recognition 36, 2339–2347 (2003)CrossRefGoogle Scholar
  6. 6.
    Jing, X., Zhang, D.: A face and palmprint recognition approach based on discriminant DCT feature extraction. IEEE Transactions on Systems, Man, and Cybernetics, Part B 34(6), 2405–2415 (2004)CrossRefGoogle Scholar
  7. 7.
    Wang, Y., Ruan, Q.: Dual-tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram Method for Palmprint Recognition? Computing and Informatics 28, 299–318 (2009)Google Scholar
  8. 8.
    Lu, G., Zhang, D., Wang, K.: Palmprint recognition using eigenpalms features. Pattern Recognition Letters 24, 1463–1467 (2003)CrossRefMATHGoogle Scholar
  9. 9.
    Yang, J., Zhang, D., Yang, J., Niu, B.: Globally maximizing, locally minimizing: Unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 650–664 (2007)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Ruan, Q.: Kernel Fisher Discriminant Analysis for Palmprint Recognition. In: The Proceedings of the 18th International Conference on Pattern Recognion (ICPR 2006), vol. 4, pp. 457–460 (2006)Google Scholar
  11. 11.
    Kong, A., Zhang, D., Kamel, M.: Palmprint identification using feature-level fusion. Pattern Recog. 39(3), 478–487 (2006)CrossRefMATHGoogle Scholar
  12. 12.
    Zhang, D., Zuo, W., Yue, F.: A comparative study of palmprint recognition algorithms. ACM Computing Surveys 44(1), Article 2, 1–37 (2007)Google Scholar
  13. 13.
    Li, D.: Uncertainty in Knowledge Representation. Engineering Science 2(10), 73–79 (2000) (in Chinese)Google Scholar
  14. 14.
    Wu, T., Qin, K.: Image segmentation using cloud model and data field. Pattern Recognition and Artificial Intelligence 25(3), 397–405 (2012)Google Scholar
  15. 15.
    Eckhorn, R., Reitboeck, H.J., et al.: Feature linking via synchronous among distributed assemblies: simulations of results from cat visual cortex. Neural. Comput. 2, 1253–1255 (1990)Google Scholar
  16. 16.
    Zhang, Y., Wu, L.: Pattern recognition via PCNN and Tsallis entropy. Sensors 8, 7518–7529 (2008)CrossRefGoogle Scholar
  17. 17.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  18. 18.
    Zhang, W., Shan, S., Gao, W., et al.: Local Gabor Binary Pattern histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV 2005), vol. 1, pp. 786–791 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yanxia Wang
    • 1
  • Jianmin Zhao
    • 1
  • Guanghua Sun
    • 1
  1. 1.College of Mathematics, Physics and Information EngineeringZhejiang Normal UniversityJinhuaChina

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