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Palmprint and Finger-Knuckle-Print for efficient person recognition based on Log-Gabor filter response

  • Abdallah Meraoumia
  • Salim Chitroub
  • Ahmed Bouridane
Article

Abstract

Person recognition systems based on biometrics are being increasingly utilized in any applications to enhance the security of physical and logical access systems. A number of biometric traits exist and are in use in various applications. Each biometric trait has its strengths and weaknesses, and the choice depends on the application. Palmprint is one of the relatively new biometrics due to its stable and unique characteristics. The rich texture information of palmprint offers one of the powerful means in person recognition. An important issue in palmprint recognition is to extract features that can discriminate an individual from the other. Recently, a novel hand-based biometric feature, Finger-Knuckle-Print (FKP), has attracted an increasing amount of attention. Like any other biometric identifiers, FKPs are believed to have the critical properties of universality, uniqueness and permanence for person recognition. In this paper, we propose a multiple traits system for person recognition using palmprint and FKP. We have used 1D Log-Gabor response to extract the information from these two traits. So, each trait is represented by the real and the imaginary templates. Such extracted templates are compared with those of the database using the Hamming distance. Using the Hong Kong Polytechnic University (PolyU) database, the experimental results showed that the proposed system achieves excellent performances in terms of computation cost and of recognition rates, for both verification and identification.

Keywords

Biometrics Palmprint Finger-Knuckle-Print 1D Log-Gabor filter Hamming distance Data fusion 

References

  1. 1.
    Sricharan, K. K., Reddy, A. A., & Ramakrishnan, A. G. (2006). Knuckle based hand correlation for user authentication. In Proceedings of SPIE biometric technology for human identification III (Vol. 62020X).Google Scholar
  2. 2.
    Kumar, A., & Zhang, D. (2010). Improving biometric authentication performance from the user quality. IEEE Transactions on Instrumentation and Measurement, 59(3), 730.Google Scholar
  3. 3.
    Kong, W. K., & Zhang, D. (2002). Palmprint texture analysis based on low-resolution images for personal authentication. In Proceedings of 16th international conference on pattern recognition (Vol. 3, pp. 807–810).Google Scholar
  4. 4.
    Li, F., Leung, M. K. H., & Yu, X. (2004). Palmprint identification using Hausdorff distance. In Proceedings of the international workshop on biomedical circuits & systems (BioCAS’04).Google Scholar
  5. 5.
    Kumar, A., Wong, D. C. M., Shen, H. C., & Jain, A. K. (2003). Personal verification using palmprint and hand geometry biometrics. In Proceedings of the 4th international conference on audio and video-based biometric personal authentication.Google Scholar
  6. 6.
    Zhao, R., Li, K., Liu, M., & Sun, X. (2009). A novel approach of personal identification based on single knuckle print image. In Proceedings of the Asia-Pacific conference on information processing (APCIP-09).Google Scholar
  7. 7.
    Zhang, D., Kong, W., You, J., & Wong, M. (2003). On-line palmprint identification. IEEE Transactions on PAMI, 25(9), 1041–1050.CrossRefGoogle Scholar
  8. 8.
    Zhang, L., Zhang, L., & Zhang, D. (2009). Finger-knuckle-print: a new biometric identifier. In Proceedings of the IEEE international conference on image processing (ICIP09).Google Scholar
  9. 9.
    Senapati, S., & Saha, G. (2007). Speaker identification by joint statistical characterization in the Log-Gabor wavelet domain. International Journal of Intelligent Systems and Technologies, 2(1), 68–76.Google Scholar
  10. 10.
    Wang, F., & Han, J. (2007). Iris recognition method using Log-Gabor filtering and feature fusion. Journal of Xian Jiaotong University, 41, 889–893.Google Scholar
  11. 11.
    Meraoumia, A., Chitroub, S., & Bouridane, A. (2009). Person’s recognition using palmprint based on 2D Gabor filter response. In J. Blanc-Talon et al. (Eds.), ACIVS 2009. LNCS 5807 (pp. 720–731). Berlin: Springer-Verlag.Google Scholar
  12. 12.
    Meraoumia, A., Chitroub, S., & Bouridane, A. (2010). Efficient person identification by fusion of multiple palmprint representations. In A. Elmoataz et al. (Eds.), ICISP 2010. LNCS 6134 (pp. 182–191). Berlin: Springer-Verlag.Google Scholar
  13. 13.
    Connie, T., Teoh, A., Goh, M., & Ngo, D. (2003). Palmprint recognition with PCA and ICA. New Zealand: Palmerston North.Google Scholar
  14. 14.
    Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4–20.Google Scholar
  15. 15.
  16. 16.
    PolyU Finger-Knuckle-Print Database. http://www4.comp.polyu.edu.hk/biometrics/FKP.htm.
  17. 17.
    Lu, G. M., Wang, K. Q., Zhang, D. (2004). Wavelet based independent component an for palmprint identification. In Proceedings of the third international conference on machine learning and cybernetics, Shanghai, Aug 26–29, 2004.Google Scholar
  18. 18.
    Wang, Y., & Ruan, Q. (2006). Kernel Fisher discriminant analysis for palmprint recognition. In Proceedings of the 18th international conference on pattern recognition.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Abdallah Meraoumia
    • 1
  • Salim Chitroub
    • 1
  • Ahmed Bouridane
    • 2
    • 3
  1. 1.Signal and Image Processing Laboratory, Telecommunication Department, Electronics and Computer Science FacultyUniversity of Science and Technology of Houari BoumedienneAlgiersAlgeria
  2. 2.Department of Computer ScienceKing Saud UniversityRiyadhSaudi Arabia
  3. 3.School of Computing, Engineering and Information SciencesNorthumbria UniversityNewcastle upon TyneUK

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