Identity Verification by Using Handprint

  • Hao Ying
  • Tan Tieniu
  • Sun Zhenan
  • Han Yufei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

In recent years, palmprint based personal identification has been extensively explored by researchers. The success of this technology has demonstrated that the inner part of palm skin is capable of distinguishing one person from another in case that proper representation is utilized. However, earlier work mainly focused on scenarios where the position and pose of hands are constrained by pegs or plates. In contrast, our purpose is to design and implement a system which is capable of recognizing an individual once he/she naturally stretches his/her hand in front of the camera. Since human hand is an articulated object, it is important to filter out geometry variations. This paper presents and compares two hand texture based personal identification methods, which are called hand-print verification in this paper to denote the idea of utilizing whole hand skin image for recognition. In one of the method, hand articulation is eliminated in a well-defined way and then the hand is treated as a whole for feature extraction, while in the other method, features are extracted in different parts of the hand, and final decision is made in a matching score level fusion manner. Experimental results for both methods are presented and compared.

Keywords

Biometrics Verification Identification Ordinal Representation Texture Analysis Handprint 
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References

  1. 1.
    Yörük, E., Dutağaci, H., Sankur, B.: Hand Biometrics. Image and Vision Comptuting 24, 483–497 (2006)CrossRefGoogle Scholar
  2. 2.
    Kumar, A., Shen, H.C.: Palmprint Identification using PalmCodes. In: Intl. Conf. Image and Graphics, IEEE, Los Alamitos (2004)Google Scholar
  3. 3.
    Kong, A.W.-K., Zhang, D.: Competitive Coding Scheme for Palmprint Verification. In: Intl. Conf. on Pattern Recognition, vol. 1, pp. 520–523 (2004)Google Scholar
  4. 4.
    Xiong, W., Toh, K.-A., Yau, W.-Y., Jiang, X.: Model-guided Deformable Hand Shape Recognition Without Positioning Aids. Patern Recognition 38, 1651–1664 (2005)CrossRefGoogle Scholar
  5. 5.
    Kong, A.W.-K., Zhang, D.: Feature Level Fusion for Effective Palmprint Authentication. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Sun, Z., Tan, T., Wang, Y., Li, S.Z.: Ordinal Palmprint Recognition for Personal Identification. Proc. of Computer Vision and Patrern Recognition (2005)Google Scholar
  7. 7.
    Ribaric, S., Fratric, I.: A Biometrics Identification System Based on Eigenpalm and Eigenfinger Features. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(11), 1698–1709 (2005)CrossRefGoogle Scholar
  8. 8.
    Wu, J., Qiu, Z.: A Hierarchical Palmprint Identification Method Using Hand Geometry and Grayscale Distribution Features. In: Intl. Conf. on Pattern Recognition, vol. 4, pp. 409–412 (2005)Google Scholar
  9. 9.
    Wolberg, G.: Digital Image Warping. IEEE Computer Society Press, Los Alamitos (1990)Google Scholar
  10. 10.
    Cula, G.O.G., Dana, K.J.: Skin Texture Modeling. Intl. Journal of Computer Vision 12(1) (2005)Google Scholar
  11. 11.
  12. 12.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hao Ying
    • 1
  • Tan Tieniu
    • 1
  • Sun Zhenan
    • 1
  • Han Yufei
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesChina

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