Pose Invariant Palmprint Recognition

  • Chhaya Methani
  • Anoop M. Namboodiri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

A palmprint based authentication system that can work with a multi-purpose camera in uncontrolled circumstances, such as those mounted on a laptop, mobile device or those for surveillance, can dramatically increase the applicability of such a system. However, the performance of existing techniques for palmprint authentication fall considerably, when the camera is not aligned with the surface of the palm. The problems arise primarily due to variations in appearance introduced due to varying pose, but is compounded by specularity of the skin and blur due to motion and focus. In this paper, we propose a method to deal with variations in pose in unconstrained palmprint imaging. The method can robustly estimate and correct variations in pose, and compute a similarity measure between the corrected test image and a reference image. Experimental results on a set of 100 user’s palms captured at varying poses show a reduction in Equal Error Eate from 22.4% to 8.7%.

Keywords

Interest Point Equal Error Rate Image Alignment Gait Recognition Palmprint Imaging 
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.

References

  1. 1.
    Kahraman, F., Kurt, B., Gokmen, M.: Robust face alignment for illumination and pose invariant face recognition. CVPR (November 2007)Google Scholar
  2. 2.
    Kale, A., Chowdhury, A.R.: Towards a view invariant gait recognition algorithm. In: IEEE Conference on AVSS, pp. 143–150 (2003)Google Scholar
  3. 3.
    Zheng, G., Wang, C.J., Boult, T.E.: Application of projective invariants in hand geometry biometrics. IEEE Transactions on Information Forensics and Security 2(4), 758–768 (2007)Google Scholar
  4. 4.
    Duta, N., Jain, A.K., Mardia, K.V.: Matching of palmprints. Pattern Recognition Letters 23, 477–485 (2002)Google Scholar
  5. 5.
    Zhang, D., Kong, W.K., You, J., Wong, M.: Online palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1041–1050 (2003)Google Scholar
  6. 6.
    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, pp. 668–678. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Sun, Z., Tan, T., Wang, Y., Li, S.Z.: Ordinal palmprint representation for personal identification. In: Proc. IEEE Computer Vision and Pattern Recognition (CVPR), pp. 279–284 (2005)Google Scholar
  8. 8.
    Doublet, J., Lepetit, O., Revenu, M.: Contact less hand recognition using shape and texture features. ICSP Proceedings 3 (2006)Google Scholar
  9. 9.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision (2000)Google Scholar
  10. 10.
    Garg, S., Sivaswamy, J., Chandra, S.: Unsupervised curvature-based retinal vessel segmentation. In: Proc. of IEEE International Symposium on Bio-Medical Imaging(ISBI), pp. 344–347 (2007)Google Scholar
  11. 11.
    Xiaowei, L., Yue, L., Yongtian, W., Dayuan, Y.: Computing homography with ransac algorithm: a novel method of registration. In: Proceedings of the SPIE, vol. 5637, pp. 109–112 (2005)Google Scholar
  12. 12.
    Poon, C., Wong, D., Shen, H.: A new method in locating and segmenting palmprint into region-of-interest. In: Proc. of 17th International Conference on Pattern Recognition (ICPR 2004), pp. 1051–1054 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chhaya Methani
    • 1
  • Anoop M. Namboodiri
    • 1
  1. 1.International Institute of Information TechnologyHyderabadIndia

Personalised recommendations