Reference Point Detection for Arch Type Fingerprints

  • Authors:

  • H. K. Lam;Z. Hou;W. Y. Yau;T. P. Chen;J. Li;K. Y. Sim

  • Affiliations:

  • Computer Vision and Image Understanding Department Institute for Infocomm Research, A*STAR (Agency for Science, Technology and Research), Fusionopolis, Singapore;Computer Vision and Image Understanding Department Institute for Infocomm Research, A*STAR (Agency for Science, Technology and Research), Fusionopolis, Singapore;Computer Vision and Image Understanding Department Institute for Infocomm Research, A*STAR (Agency for Science, Technology and Research), Fusionopolis, Singapore;Computer Vision and Image Understanding Department Institute for Infocomm Research, A*STAR (Agency for Science, Technology and Research), Fusionopolis, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

  • Venue:
  • ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
  • Year:
  • 2009

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Abstract

Reference point detection is an important task in the design of an automated fingerprint identification system. Many algorithms have emerged with acceptable results but are mostly suitable for non-arch type fingerprint. It still remains as a challenging problem to reliably identify reference points for fingerprints of the arch type. A topological method is presented in this paper to detect reference points in arch type fingerprint images. To evaluate the performance, 400 arch type fingerprint image pairs in the NIST DB4 database are utilized. The alignment accuracy on average is about 35 pixels in distance and 9 degrees in orientation, which is very well comparable with respect to state-of-the-arts as designed for non-arch type fingerprints.