Multiset Canonical Correlation Analysis: Texture Feature Level Fusion of Multiple Descriptors for Intra-modal Palmprint Biometric Recognition

  • Raouia Mokni
  • Anis Mezghani
  • Hassen Drira
  • Monji Kherallah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)

Abstract

This paper describes a novel intra-modal feature fusion for palmprint recognition based on fusing multiple descriptors to analyze the complex texture pattern. The main contribution lies in the combination of several texture features extracted by the Multi-descriptors, namely: Gabor Filters, Fractal Dimension and Gray Level Concurrence Matrix. This means to their effectiveness to confront the various challenges in terms of scales, position, direction and texture deformation of palmprint in unconstrained environments. The extracted Gabor filter-based texture features from the preprocessed palmprint images to be fused with the Fractal dimension-based-texture features and Gray Level Concurrence Matrix-based texture features using the Multiset Canonical Correlation Analysis method (MCCA). Realized experiments on three benchmark datasets prove that the proposed method surpasses other well-known state of the art methods and produces encouraging recognition rates by reaching 97.45% and 96.93% for the PolyU and IIT-Delhi Palmprint datasets.

Keywords

Palmprint Texture analysis Gabor Filters Fractal Dimension Gray Level Concurrence Matrix Information fusion Multiset Canonical Correlation Analysis (MCCA) 

References

  1. 1.
  2. 2.
    Ahmad, M.I., Ilyas, M.Z., Ngadiran, R., Isa, M.N.M., Yaakob, S.N.: Palmprint recognition using local and global features. In: IWSSIP, pp. 79–82 (2014)Google Scholar
  3. 3.
    Chaabouni, A., Boubaker, H., Kherallah, M., Alimi, A.M., El Abed, H.: Fractal and multi-fractal for Arabic offline writer identification. In: 20th International Conference on Pattern Recognition, pp. 3793–3796. IEEE (2010)Google Scholar
  4. 4.
    Chen, J., Zhang, C., Rong, G.: Palmprint recognition using crease. In: 2001 Proceedings of International Conference on Image Processing, vol. 3, pp. 234–237. IEEE (2001)Google Scholar
  5. 5.
    Dai, J., Feng, J., Zhou, J.: Robust and efficient ridge-based palmprint matching. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1618–1632 (2012)CrossRefGoogle Scholar
  6. 6.
    Fractals, M.B.L.O.: Forme, hasard et dimension. Flammarion, Paris (1975)Google Scholar
  7. 7.
    Guo, X., Zhou, W., Wang, Y.: Palmprint recognition algorithm with horizontally expanded blanket dimension. Neurocomputing 127, 152–160 (2014)CrossRefGoogle Scholar
  8. 8.
    Haghighat, M., Abdel-Mottaleb, M., Alhalabi, W.: Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Syst. Appl. 47, 23–34 (2016)CrossRefGoogle Scholar
  9. 9.
    Haghighat, M., Zonouz, S., Abdel-Mottaleb, M.: Identification using encrypted biometrics. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8048, pp. 440–448. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40246-3_55 CrossRefGoogle Scholar
  10. 10.
    Haghighat, M., Zonouz, S., Abdel-Mottaleb, M.: Cloudid: trustworthy cloud-based and cross-enterprise biometric identification. Expert Syst. Appl. 42(21), 7905–7916 (2015)CrossRefGoogle Scholar
  11. 11.
    Hammami, M., Jemaa, S.B., Ben-Abdallah, H.: Selection of discriminative sub-regions for palmprint recognition. Multimedia Tools Appl. 68(3), 1023–1050 (2014)CrossRefGoogle Scholar
  12. 12.
    Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  13. 13.
    IIT Delhi (IIoTD): IIT Delhi Touchless Palmprint Database (Version 1.0) (2014). http://web.iitd.ac.in/~ajaykr/Database_Palm.htm
  14. 14.
    Jollie, I.: Principal Component Analysis. Wiley, Hoboken (2002)Google Scholar
  15. 15.
    Krzanowski, W.: Principles of Multivariate Analysis: A User’s Perspective. Clarendon, New York (1988)MATHGoogle Scholar
  16. 16.
    Kumar, A., Shekhar, S.: Palmprint recognition using rank level fusion. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3121–3124. IEEE (2010)Google Scholar
  17. 17.
    Latha, Y.M., Prasad, M.V.: Intramodal palmprint recognition using texture feature. Int. J. Intell. Syst. Des. Comput. 1(1-2), 168–185 (2017)Google Scholar
  18. 18.
    Lee, T.S.: Image representation using 2D gabor wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18(10), 959–971 (1996)CrossRefGoogle Scholar
  19. 19.
    Mokni, R., Elleuch, M., Kherallah, M.: Biometric palmprint identification via efficient texture features fusion. In: International Joint Conference on Neural Networks, pp. 4857–4864 (2016)Google Scholar
  20. 20.
    Mokni, R., Drira, H., Kherallah, M.: Combining shape analysis and texture pattern for palmprint identification. Multimedia Tools Appl. 76, 1–28 (2016).  https://doi.org/10.1007/s11042-016-4088-5 Google Scholar
  21. 21.
    Mokni, R., Drira, H., Kherallah, M.: Fusing multi-techniques based on LDA-CCA and their application in palmprint identification system. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). IEEE (2017, in press)Google Scholar
  22. 22.
    Mokni, R., Kherallah, M.: Palmprint recognition through the fractal dimension estimation for texture analysis. Int. J. Biometr. 8(3–4), 254–274 (2016)CrossRefGoogle Scholar
  23. 23.
    Mokni, R., Zouari, R., Kherallah, M.: Pre-processing and extraction of the ROIs steps for palmprints recognition system. In: International Conference on Intelligent Systems Design and Applications, pp. 380–385 (2015)Google Scholar
  24. 24.
    Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)Google Scholar
  25. 25.
    Ribarić, S., Lopar, M.: Palmprint recognition based on local texture features. In: 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2013, International Workshop on Intelligent Pattern Recognition and Applications, WIPRA 2013 (2013)Google Scholar
  26. 26.
    Sun, Q.S., Zeng, S.G., Liu, Y., Heng, P.A., Xia, D.S.: A new method of feature fusion and its application in image recognition. Pattern Recogn. 38(12), 2437–2448 (2005)CrossRefGoogle Scholar
  27. 27.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media, New York (1995).  https://doi.org/10.1007/978-1-4757-3264-1
  28. 28.
    Wang, X., Lei, L., Wang, M.: Palmprint verification based on 2D-gabor wavelet and pulse-coupled neural network. Knowl.-Based Syst. 27, 451–455 (2012)CrossRefGoogle Scholar
  29. 29.
    Yang, J.: Yang, J.y., Zhang, D., Lu, J.f.: Feature fusion: parallel strategy vs. serial strategy. Pattern Recogn. 36(6), 1369–1381 (2003)Google Scholar
  30. 30.
    Zouari, R., Mokni, R., Kherallah, M.: Identification and verification system of offline handwritten signature using fractal approach. In: 1st International Image Processing, Applications and Systems Conference, pp. 1–4. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Economics and Management of SfaxUniversity of SfaxSfaxTunisia
  2. 2.University of SfaxSfaxTunisia
  3. 3.Institut Mines-Télécom/Télécom Lille, CRIStAL (UMR CNRS 9189)LilleFrance
  4. 4.Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia

Personalised recommendations