Biometric Recognition Systems Using Multispectral Imaging

  • Abdallah Meraoumia
  • Salim Chitroub
  • Ahmed Bouridane
Part of the Intelligent Systems Reference Library book series (ISRL, volume 70)


Automatic personal identification is playing an important role in secure and reliable applications, such as access control, surveillance systems, information systems, physical buildings and many more applications. In contrast with traditional approaches, based on what a person knows (password) or what a person has (tokens), biometric based identification providing an improved security for their users. Biometrics is the measurement of physiological traits such as palmprints, fingerprints, iris etc., and/or behavioral traits such as gait, signature etc., of an individual person for personal recognition. Hand-based person identification provides a good user acceptance, distinctiveness, universality, relatively easy to capture, low-cost and inexpensive. Palmprint identification is one kind of hand-biometric technology and a relatively new biometrics due to its stable and unique traits. The rich texture information of palmprint offers one of the powerful means in personal identification. Several studies for palmprint-based person identification have focused on the use of palmprint images captured in the visible part of the spectral band. However, recently, the multispectral palmprints have been rendered available and the tendency now in the community is how to exploit these multispectral data to improve the performances of the palmprint-based person identification systems. In this chapter, we try to evaluate the usefulness of the multispectral palmprints for improving the palmprint based person identification systems. For that purpose, we propose several systems of exploiting the multispectral palmprints. The results on a medium-size database show good identification performance based on individual modalities as well as after fusing multiple spectral bands.


Spectral Band Equal Error Rate Biometric System False Reject Rate Multimodal System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Arun, A., Ross, A., Nandakumar, K., Jain, A.K.: Handbook of multibiometrics. In: Springer Science+Business Media, LLC, New York (2006)Google Scholar
  2. 2.
    Wayman, J., Jain, A., Maltoni, D., Maio, D.: Biometric Systems, Technology, Design and Performance Evaluation. Springer, London (2005)Google Scholar
  3. 3.
    Jain, A.K., Ross, A., Pankanti, S.: Biometrics: a tool for information security. IEEE Trans. Inf. Forensics Secur. 1(2), 125–143 (2006)Google Scholar
  4. 4.
    Meraoumia, A., Chitroub, S., Ahmed, B.: Multimodal biometric person recognition system based on multi-spectral palmprint features using fusion of wavelet representations. In: Advanced Biometric Technologies. Published by InTech, pp. 21–42 (2011). ISBN 978-953-307-487-0Google Scholar
  5. 5.
    Zhang N.: Face recognition based on classifier combinations. In: International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM), Guiyang, China, 267–270, (2011)Google Scholar
  6. 6.
    Han, D., Guo, Z., Zhang, D.: Multispectral palmprint recognition using wavelet-based image fusion. In: proceedings of the 9th International Conference on Signal Processing, pp. 2074–2077 (2008)Google Scholar
  7. 7.
    Guo, Z., Zhang, D., Zhang, L.: Is white light the best illumination for palmprint recognition? In: Computer Analysis of Images and Patterns Lecture Notes in Computer Science, vol. 5702, 50–57 (2009)Google Scholar
  8. 8.
    Singh, R., Vatsa, M., Noore, A.: Hierarchical fusion of multispectral face images for improved recognition performance. Inf. Fusion 9(2), 200210 (2008)Google Scholar
  9. 9.
    Zhang, D., Guo, Z., Guangming, L., Zhang, L., Zuo, W.: An online system of multispectral palmprint verification. IEEE Trans. Instrum. Measur. 59(2), 480–490 (2010)Google Scholar
  10. 10.
    Khan, Z., Mian, A., Hu, Y.: Contour code: robust and efficient multispectral palmprint encoding for human recognition. In: ICCV2011 (2011)Google Scholar
  11. 11.
    Cui, J.-R.: Multispectral palmprint recognition using Image? Based linear discriminant analysis. Int. J. Biometrics 4(2), 106–115 (2012)Google Scholar
  12. 12.
    Xu, X., Guo, Z., Song, C., Li, Y.: Multispectral palmprint recognition using a quaternion matrix. Sensors 12(4), 4633–4647 (2012)CrossRefGoogle Scholar
  13. 13.
    Bogoni, L., Hansen, M.: Pattern-selective color image fusion. Pattern Recogn. 34(8), 1515–1526 (2006)Google Scholar
  14. 14.
    Simone, G., Farina, A., Morabito, F.C., Serpico, S.B., Bruzzone, L.: Image fusion techniques for remote sensing applications. Inf. Fusion 3(1), 3–15 (2002)CrossRefGoogle Scholar
  15. 15.
    Jain, A.K., Ross, A.: Learning user-specific parameters in a multibiometric system. In: Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 57–60, Rochester, NY (2002)Google Scholar
  16. 16.
    Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recogn. 38, 2270–2285 (2005)Google Scholar
  17. 17.
    Jiaa, W., Huang, D.-S., Zhang, D.: Palmprint verification based on robust line orientation code. Pattern Recogn. 41, 1504–1513 (2008)Google Scholar
  18. 18.
    PolyU Database. The Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database (2003).
  19. 19.
    Zhang, D., Kong, A.W.K., You, J., Wong, M.: On-line palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1041–1050 (2003)CrossRefGoogle Scholar
  20. 20.
    Singh, A.P., Mishra, A.: Image de-noising using contoulets (a comparative study with wavelets). Int. J. Adv. Networking Appl. 03(03), 1210–1214 (2011)Google Scholar
  21. 21.
    Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. In: IEEE ASSP Magazine, pp. 4–16 (1986)Google Scholar
  22. 22.
    Uguz, H., Arslan, A., Turkoglu, I.: A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases. Pattern Recogn. Lett. 28, 395–404 (2007)Google Scholar
  23. 23.
    Viterbi, A.J.: A personal history of the Viterbi algorithm. In: IEEE Signal Processing Magazine, pp. 120–142 (2006)Google Scholar
  24. 24.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Networks 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  25. 25.
    Hussain, A., Ghafar, R., Samad, S.A., Tahir, N.M.: Anomaly detection in electroencephalogram signals using unconstrained minimum average correlation energy filter. J. Comput. Sci. 5(7), 501–506 (2009)Google Scholar
  26. 26.
    Ghafar, R., Hussain, A., Samad, S.A., Tahir, N.M.: Umace filter for detection of abnormal changes in eeg: a report of 6 cases. World Appl. Sci. J. 5(3), 295–301 (2008)Google Scholar
  27. 27.
    Senapati, S., Saha, G.: Speaker identification by joint statistical characterization in the Log-Gabor wavelet domain. In: International Journal of Intelligent Systems and Technologies, Winter (2007)Google Scholar
  28. 28.
    Wang, F., Han, J.: Iris recognition method using Log-Gabor filtering and feature fusion. J. Xian Jiaotong Univ. 41, 360–369 (2007)Google Scholar
  29. 29.
    Meraoumia, A., Chitroub, S., Saigaa, M.: Person’s recognition using palmprint 2 based on 2D gabor filter response. In: Advanced Concepts for Intelligent Vision Systems. International conference, ACIVS 2009, Bordeaux, France, September 28 October 2, 2009. Proceedings. Berlin, Springer, LNCS 5807, 720–731 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Abdallah Meraoumia
    • 1
  • Salim Chitroub
    • 2
  • Ahmed Bouridane
    • 3
  1. 1.Faculté des nouvelles technologies de l’information et de la communication, Laboratoire de Génie ÉlectriqueUniversité de OuarglaOuarglaAlgeria
  2. 2.Image Processing Laboratory, Electronics and Computer Science FacultyUSTHBAlgiersAlgeria
  3. 3.School of Computing, Engineering and Information SciencesNorthumbria UniversityNewcastle upon TyneUK

Personalised recommendations