Local Orientation Binary Pattern with Use for Palmprint Recognition

  • Lunke Fei
  • Yong Xu
  • Shaohua Teng
  • Wei Zhang
  • Wenliang Tang
  • Xiaozhao Fang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10568)

Abstract

In this paper, we extensively exploit the discriminative orientation features of palmprint, including the principal orientation and corresponding orientation confidence, and further propose a local orientation binary pattern (LOBP) for palmprint recognition. Different from the existing binary based representation methods, the LOBP method first captures the principal orientation consistency by comparing the center point with the neighbor sets, and then captures the confidence variations by thresholding the center confidence with neighborhoods so as to obtain orientation binary pattern (OBP) and confidence binary pattern (CBP), respectively. Furthermore, the block-wise statistics of OBP and CBP are concentrated to generate a novel descriptor, namely LOBP, of palmprint. Experiment results on different types of palmprint databases demonstrate the effectiveness of the proposed method.

Keywords

Biometric Palmprint recognition Orientation binary pattern Confidence binary pattern 

Notes

Acknowledgment

This paper is partially supported by Guangdong Province high-level personnel of special support program (No. 2016TX03X164), and Shenzhen Fundamental Research fund (JCYJ20160331185006518).

References

  1. 1.
    Jia, W., Zhang, B., Lu, J., Zhu, Y., Zhao, Y., Zuo, W., Ling, H.: Palmprint recognition based on complete direction representation. IEEE Trans. Image Process. 26(9), 4483–4498 (2017)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Fei, L., Zhang, B., Xu, Y., Yan, L.: Palmprint recognition using neighboring direction indicator. IEEE Trans. Hum.-Mach. Syst. 46(6), 787–798 (2016)CrossRefGoogle Scholar
  3. 3.
    Jain, A.K., Feng, J.: Latent palmprint matching. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1032–1047 (2009)CrossRefGoogle Scholar
  4. 4.
    Huang, D.S., Jia, W., Zhang, D.: Palmprint verification based on principal lines. Pattern Recogn. 41, 1316–1328 (2008)CrossRefGoogle Scholar
  5. 5.
    Fei, L., Xu, Y., Tang, W., Zhang, D.: Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recogn. 49, 89–101 (2016)CrossRefGoogle Scholar
  6. 6.
    Kong, A.K., Zhang, D.: Competitive coding scheme for palmprint verification. In: 17th International Conference on Pattern Recognition, pp. 520–523 (2004)Google Scholar
  7. 7.
    Sun, Z., Tan, T., Wang, Y., Li, S.: Ordinal palmprint represention for personal identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 279–284 (2005)Google Scholar
  8. 8.
    Fei, L., Xu, Y., Zhang, D.: Half-orientation extraction of palmprint features. Pattern Recogn. Lett. 69, 35–41 (2016)CrossRefGoogle Scholar
  9. 9.
    Xu, Y., Fei, L., Wen, J., Zhang, D.: Discriminative and robust competitive code for palmprint recognition. IEEE Trans. Syst. Man Cybern.: Syst. (2016). doi:10.1109/TSMC.2016.2597291.
  10. 10.
    Zhang, D., Zuo, W., Yue, F.: A comparative study of palmprint recognition algorithms. ACM Comput. Surv. 44, 1–37 (2012)CrossRefGoogle Scholar
  11. 11.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)CrossRefMATHGoogle Scholar
  12. 12.
    Huang, D., Shan, C.F., Ardabilian, M., Wang, Y.H.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41, 765–781 (2011)Google Scholar
  13. 13.
    Michael, G., Connie, T., Teoh, A.: Touch-less palm print biometrics: novel design and implementation. Image Vis. Comput. 26, 1551–1560 (2008)CrossRefGoogle Scholar
  14. 14.
    Guo, Z., Zhang, D., Zhang, L., Zuo, W.: Palmprint verification using binary orientation co-occurrence vector. Pattern Recogn. Lett. 30, 1219–1227 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, L., Li, H., Niu, J.: Fragile bits in palmprint recognition. IEEE Sig. Process. Lett. 19, 663–666 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lunke Fei
    • 1
  • Yong Xu
    • 2
  • Shaohua Teng
    • 1
  • Wei Zhang
    • 1
  • Wenliang Tang
    • 3
  • Xiaozhao Fang
    • 1
  1. 1.School of Computer Science and TechnologyGuangdong University of TechnologyGuangzhouChina
  2. 2.Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina
  3. 3.School of SoftwareEast China Jiaotong UniversityNanchangChina

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