Comparison of Distance-Based Features for Hand Geometry Authentication

  • Javier Burgues
  • Julian Fierrez
  • Daniel Ramos
  • Javier Ortega-Garcia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5707)


A hand-geometry recognition system is presented. The development and evaluation of the system includes feature selection experiments using an existing publicly available hand database (50 users, 500 right hand images). The obtained results show that using a very small feature vector high recognition rates can be achieved. Additionally, various experimental findings related to feature selection are obtained. For example, we show that the least discriminative features are related to the palm geometry and thumb shape. A comparison between the proposed system and a reference one is finally given, showing the remarkable performance obtained in the present development when considering the best feature combination.


Hand geometry biometrics feature selection 


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  1. 1.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. on Circuits and Systems for Video Technology. 14, 4–20 (2004)CrossRefGoogle Scholar
  2. 2.
    Sanchez-Reillo, R., Sanchez-Avila, C., Gonzalez-Marcos, A.: Biometric identification through hand geometry measurements. IEEE Trans. on Pattern Analysis and Machine Intelligence. 22, 1168–1171 (2000)CrossRefGoogle Scholar
  3. 3.
    Yörük, E., Konukoglu, E., Sankur, B.: Shape-Based Hand Recognition. IEEE Trans. on Image Processing. 15, 1803–1815 (2006)CrossRefGoogle Scholar
  4. 4.
    Oden, C., Ercil, A., Buke, B.: Combining implicit polynomials and geometric features for hand recognition. Patter Recognition Letter 24, 2145–2152 (2003)CrossRefGoogle Scholar
  5. 5.
    Zhang, D., Kong, W.K., You, J., Wong, M.: Online Palmprint Identification. IEEE Trans. on Pattern Analysis and Machine Intelligence. 25, 1041–1050 (2003)CrossRefGoogle Scholar
  6. 6.
    Kumar, A., Wong, D.C.M., Shen, H.C., Jain, A.K.: Personal authentication using hand images. Pattern Recognition Letters 27, 1478–1486 (2006)CrossRefGoogle Scholar
  7. 7.
    Geoffroy, F., Likforman, L., Darbon, J., Sankur, B.: The Biosecure geometry-based system for hand modality. In: ICASSP, vol. 147, pp. 195–197 (2007)Google Scholar
  8. 8.
    González, S., Travieso, C.M., Alonso, J.B., Ferrer, M.A.: Automatic biometric identification system by hand geometry. In: Proceedings. IEEE 37th Annual 2003 International Carnahan Conference Security Technology, 2003, pp. 281–284 (2003)Google Scholar
  9. 9.
    Dutagaci, H., Fouquier, G., Yoruk, E., Sankur, B., Likforman-Sulem, L., Darbon, J.: Hand Recognition. In: Petrovska-Delacretaz, D., Chollet, G., Dorizzi, B. (eds.) Guide to Biometric Reference Systems and Performance Evaluation. Springer, London (2008)Google Scholar
  10. 10.
    Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: The DET curve in assessment of detection task performance. In: EUROSPEECH 1997, pp. 1895–1898 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Javier Burgues
    • 1
  • Julian Fierrez
    • 1
  • Daniel Ramos
    • 1
  • Javier Ortega-Garcia
    • 1
  1. 1.ATVS - Biometric Recognition Group, EPSUniversidad Autonoma de MadridMadridSpain

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