Preprocessing of a Fingerprint Image Captured with a Mobile Camera

  • Chulhan Lee
  • Sanghoon Lee
  • Jaihie Kim
  • Sung-Jae Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

A preprocessing algorithm of a fingerprint image captured with a mobile camera is proposed. Fingerprint images from a mobile camera are different from images from conventional or touch-based sensors such as optical, capacitive, and thermal sensors. For example, images from a mobile camera are colored and the backgrounds or non-finger regions can be very erratic depending on how the image captures time and place. Also, the contrast between the ridges and valleys of images from a mobile camera is lower than that of images from touch-based sensors. Because of these differences between the input images, a new and modified fingerprint preprocessing algorithm is required for fingerprint recognition when using images captured with a mobile camera.

Keywords

Background Region Fingerprint Image Orientation Estimation False Acceptance Rate Preprocessing Algorithm 
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.
    Elli, A.: Understanding the Color of Human Skin. In: Proceedings of the 2001 SPIE conference on Human Vision and Electronic Imaging VI, SPIE, May 2001, vol. 4299, pp. 243–251 (2001)Google Scholar
  2. 2.
    Zarit, B.D., Super, B.J., Quek, F.K.H.: Comparison of Five Color Models in Skin Pixel Classification. In: International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 58–63 (1999)Google Scholar
  3. 3.
    Chern, N.K., Neow, P.A., Ang Jr., M.H.: Practical issues in pixel-based autofocusing for machine vision. In: Int. Conf. On Robotics and Automation, pp. 2791–2796 (2001)Google Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn., p. 613. Addison-Wesley, Reading (2002)Google Scholar
  5. 5.
    Nalini, K., Ratha, Shaoyun, C., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognition 28(11), 1657–1672 (1995)CrossRefGoogle Scholar
  6. 6.
    Bazen, A.M., Gerez, S.H.: Directional field computation for fingerprints based on the principal component analysis of local gradients. In: Proceedings of ProRISC 2000, 11th Annual Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands (November 2000)Google Scholar
  7. 7.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint Image Enhancement: Algorithms and Performance Evaluation. IEEE Transactions on PAMI 20(8), 777–789 (1998)Google Scholar
  8. 8.
    Lee, D., Choi, K., Kim, J.: A Robust Fingerprint Matching Algorithm Using Local Alignment. In: International Conference on Pattern Recognition, Quebec, Canada (August 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chulhan Lee
    • 1
  • Sanghoon Lee
    • 1
  • Jaihie Kim
    • 1
  • Sung-Jae Kim
    • 2
  1. 1.Biometrics Engineering Research Center, Department of Electrical and Electronic EngineeringYonsei UniversitySeoulKorea
  2. 2.Multimedia Lab., SOC R&D centerSamsung Electronics Co., LtdGyeonggi-DoKorea

Personalised recommendations