Multimodal Hand-Palm Biometrics

  • Ryszard S. Choraś
  • Michał Choraś
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)


Hand geometry based biometric verification has proven to be the most suitable and acceptable biometrics trait for medium and low security applications. Hereby a new approach for the personal identification using hand images is presented. Two kinds of biometric indicators are extracted from the low-resolution hand images; (i) palmprint features, which are composed of principal lines, wrinkles, minutiae, delta points, etc., and (ii) hand geometry features which include area/size of palm, length and width of fingers. In the article we focus on feature extraction methods applied to one-sensor multimodal hand-palm biometrics system.


Feature Extraction Method Zernike Moment Biometric System Hand Image Principal Line 
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.


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  1. 1.
    Khotanzad, A.: Invariant image recognition by Zernike moments. IEEE Trans. Pattern. Anal. Machine Intell. 12, 489–497 (1990)CrossRefGoogle Scholar
  2. 2.
    Mukundan, R., Ramakrishnan, K.R.: Moment Functions in Image Analysis   Theory and Applications. World Scientific, Singapore (1998)zbMATHGoogle Scholar
  3. 3.
    Sanchez-Reillo, R., Sanchez-Avila, C., Gonzales-Marcos, A.: Biometric Identification through Hand Geometry Measurements. IEEE Trans. On Pattern Analysis and Machine Intelligence 22(10), 1168–1171 (2000)CrossRefGoogle Scholar
  4. 4.
    Jain, A.K., Ross, A., Pankarti, S.: A prototype hand geometry based verification system. In: Proc. AVBPA, Washington D.C., March 1999, pp. 166–171 (1999)Google Scholar
  5. 5.
    Bulatov, Y., Jambawalikar, S., Kumar, P., Sethia, S.: Hand recognition using geometric classifiers. In: DIMACS Workshop on Computational Geometry, Rutgers University, Piscataway, NJ (2002)Google Scholar
  6. 6.
    Oden, C., Ercil, A., Buke, B.: Combining implicit polynomials and geometric features for hand recognition. Pattern Recognition Letters 24, 2145–2152 (2003)CrossRefGoogle Scholar
  7. 7.
    Lay, Y.L.: Hand shape recognition. Optics and Laser Technology 32(1), 1–5 (2000)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Saeed, K., Werdoni, M.: A New Approach for Hand-Palm Recognition. In: Pejaś, J., Piegat, A. (eds.) Enhancement Methods in Computer Security – Biometric and Artificial Intelligence Systems, Springer Science + Business Media, New York (2005)Google Scholar
  9. 9.
    Han, C.C., Cheng, H.L., Lin, C.L., Fan, K.C.: Personal Authentication using Palmprint Features. Pattern Recognition 36, 371–381 (2003)CrossRefGoogle Scholar
  10. 10.
    Kumar, A., Wong, D.C.M., Shen, H.C., Jain, A.K.: Personal Verification using Palmprint and Hand Geometry Biometric. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, p. 668. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Kumar, A., Shen, H.C.: Recognition of Palmprints using Wavelet-based Features. In: Proc. of Intl. Conf. on Systems and Cybernetics (2002)Google Scholar
  12. 12.
    Li, W., Zhang, D., Xu, Z.: Palmprint Recognition by Fourier Transform. Journal of Software 13(5), 879–886 (2002)Google Scholar
  13. 13.
    Li, W., Xia, S., Zhang, D., Xu, Z.: A New Palmprint Segmentation Method Based on an Inscribed Circle. Image Processing and Communications 9(1), 63–70 (2002)Google Scholar
  14. 14.
    You, J., Li, W., Zhang, D.: Hierarchical Palmprint Identification via Multiple Feature Extraction. Pattern Recognition 35, 847–859 (2002)zbMATHCrossRefGoogle Scholar
  15. 15.
    Zhang, D., Shu, W.: Two Novel Characteristics in Palmprint Verification: Datum Point Invariance and Line Feature Matching. Pattern Recognition 33(4), 691–702 (1999)CrossRefGoogle Scholar
  16. 16.
    Canny, J.F.: A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  17. 17.
    Chen, J., Zhang, C., Rong, G.: Palmprint recognition using crease. In: Proc. Intl. Conf. Image Process, Oct. 2001, pp. 234–237 (2001)Google Scholar
  18. 18.
    Liao, S.X., Pawlak, M.: On the accuracy of zemike moments for image analysis. IEEE Trans. Pattern. Anal. Machine Intell. 20, 1358–1364 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Ryszard S. Choraś
    • 1
  • Michał Choraś
    • 1
  1. 1.Image Processing Group, Institute of Telecommunications, University of Technology & Life Sciences, S. Kaliskiego 7, 85-791 BydgoszczPoland

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