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Machine Vision and Applications

, Volume 28, Issue 3–4, pp 283–291 | Cite as

Collaborative representation with HM-LBP features for palmprint recognition

Original Paper

Abstract

A novel collaborative representation model with hierarchical multiscale local binary pattern (HM-LBP) for palmprint recognition is proposed in this paper. HM-LBP can retrieve useful information from non-uniform patterns and reduce the influence of gray scale, rotation and illumination. The HM-LBP feature of palmprint is extracted, and its dimension is reduced by principal component analysis. And then, a collaborative classification with HM-LBP is presented to fully exploit the discrimination information. The proposed algorithm is evaluated on the Hong Kong Polytechnic University database (v2) to test its feasibility and performance. The results show that the algorithm can achieve ideal recognition accuracy of 100% and the speediness is able to fit for the real-time palmprint recognition system.

Keywords

Collaborative representation Hierarchical multiscale local binary pattern Palmprint recognition 

References

  1. 1.
    Shu, W., Zhang, D.: Palmprint verification: an implementation of biometric technology. In: International Conference on Pattern Recognition. IEEE Computer Society, vol. 1, pp. 219–221 (1998)Google Scholar
  2. 2.
    Guo, X.M., Wan, L.M.: A palmprint recognition based on collaborative representation. Appl. Mech. Mater. 457–458, 1317–1322 (2014)Google Scholar
  3. 3.
    Ojala, T., Pietik, Inen, M., et al.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE. Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Google Scholar
  4. 4.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 19(6), 168–182 (2010)MathSciNetGoogle Scholar
  5. 5.
    Liao, S., Law, M.W., Chung, A.C.: Dominant local binary patterns for texture classification. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 18(5), 1107–1118 (2009)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognit. 42(3), 425–436 (2009)CrossRefMATHGoogle Scholar
  7. 7.
    Zhang, B., Gao, Y., Zhao, S., et al.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 19(6), 1657–1663 (2010)MathSciNetGoogle Scholar
  9. 9.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefMATHGoogle Scholar
  10. 10.
    Zhang, G., Huang, X., Li, S.Z., et al.: Boosting local binary pattern (LBP)-based face recognition advances in biometric person authentication. In: Chinese Conference on Biometric Recognition, Sino Biometrics 2004, Guangzhou, China, December 13–14, 2004, Proceedings, pp. 179–186 (2004)Google Scholar
  11. 11.
    Wang, X.J., Gong, H., Zhang, H., et al.: Palmprint identification using boosting local binary pattern recognition. In: International Conference on. IEEE Computer Society, pp. 503–506 (2006)Google Scholar
  12. 12.
    Michael, G.K.O., Connie, T., Teoh, A.B.J.: Touch-less palm print biometrics: novel design and implementation. Image Vis. Comput. 26(12), 1551–1560 (2008)CrossRefGoogle Scholar
  13. 13.
    Raja, Y., Sparse, G.S.: Binary, Multiscale Local, Patterns. British Machine Vision Conference, Edinburgh, September, pp. 799–808 (2006)Google Scholar
  14. 14.
    Liao, S., Zhu, X., Lei, Z., et al.: Learning multi-scale block local binary patterns for face recognition. Adv. Biom. 4642, 828–837 (2007)CrossRefGoogle Scholar
  15. 15.
    Guo, Z., Zhang, L., Zhang, D., et al.: Hierarchical multiscale LBP for face and palmprint recognition. In: IEEE International Conference on Image Processing. IEEE, pp. 4521–4524 (2010)Google Scholar
  16. 16.
    Huang, J., Huang, X., Metaxas, D.: Simultaneous image transformation and sparse representation recovery. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  17. 17.
    Mairal, J., Bach, F., Ponce, J., et al.: Non-local sparse models for image restoration. In: IEEE International Conference on Computer Vision, pp. 2272–2279 (2009)Google Scholar
  18. 18.
    Gao, S., Tsang, W.H., Chia, L.T.: Kernel Sparse Representation for Image Classification and Face Recognition European Conference on Computer Vision, pp. 1–14. Springer (2010)Google Scholar
  19. 19.
    Yang, M., Zhang, L.: Gabor feature based sparse representation for face recognition with gabor occlusion dictionary computer vision ECCV 2010. In: European Conference on Computer Vision, Heraklion, Crete, Greece, September 5–11, 2010, Proceedings, pp. 448–461 (2010)Google Scholar
  20. 20.
    Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: International Conference on Computer Vision. IEEE Computer Society, pp. 471–478 (2011)Google Scholar
  21. 21.
    Doshi, N.P., Schaefer, G.: A Comprehensive Benchmark of Local Binary Pattern Algorithms for Texture Retrieval 21\({st}\) International Conference on Pattern Recognition (ICPR2012), pp. 2760–2763 (2012)Google Scholar
  22. 22.
    Raghavendra, R., Busch, C.: Robust palmprint verification using sparse representation of binarized statistical features: a comprehensive study. In: ACM Workshop on Information Hiding and Multimedia Security. ACM, pp. 181–185 (2014)Google Scholar
  23. 23.
    Lu, G., Zhang, D., Wang, K.: Palmprint recognition using eigenpalms features. Pattern Recognit. Lett. 24(9–10), 1463–1467 (2003)CrossRefMATHGoogle Scholar
  24. 24.
    Kong, A., Zhang, D., Kamel, M.: Palmprint identification using feature-level fusion. Pattern Recognit. 39(3), 478–487 (2006)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringShandong UniversityJinanChina
  2. 2.School of Information Science and EngineeringShandong Agricultural UniversityTaianChina

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