Characterisation of Retinal Feature Points Applied to a Biometric System

  • David Calvo
  • Marcos Ortega
  • Manuel G. Penedo
  • José Rouco
  • Beatriz Remeseiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

In this work a methodology for the classification of retinal feature points is applied to a biometric system. This system is based in the extraction of feature points, namely bifurcations and crossovers as biometric pattern. In order to compare a pattern to other from a known individual a matching process takes place between both points sets. That matching task is performed by finding the best geometric transform between sets, i.e. the transform leading to the highest number of matched points. The goal is to reduce the number of explored transforms by introducing the previous characterisation of feature points. This is achieved with a constraint avoiding two differently classified points to match. The empirical reduction of transforms is about 20%.

Keywords

Retinal verification Feature points characterisation Registration 

References

  1. 1.
    Mariño, C., Penedo, M.G., Penas, M., Carreira, M.J., González, F.: Personal authentication using digital retinal images. Pattern Analysis and Applications 9, 21–33 (2006)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Tan, X., Bhanu, B.: A robust two step approach for fingerprint identification. Pattern Recognition Letters 24, 2127–2134 (2003)CrossRefGoogle Scholar
  3. 3.
    Ortega, M., Mariño, C., Penedo, M.G., Blanco, M., González, F.: Personal authentication based on featue extraction and optica nerve location in digital retinal images. WSEAS Transactions on Computers 5(6), 1169–1176 (2006)Google Scholar
  4. 4.
    Ortega, M., Penedo, M.G., Mariño, C., Carreira, M.J.: Similarity metrics analysis for feature point based retinal authentication. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 1023–1032. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Tsai, C.L., Stewart, C., Tanenbaum, H., Roysam, B.: Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images. IEEE Transactions on Information Technology in Biomedicine 8(2), 122–130 (2004)CrossRefGoogle Scholar
  6. 6.
    Bevilacqua, V., Cambó, S., Cariello, L., Mastronardi, G.: A combined method to detect retinal fundus features. In: Proceedings of IEEE European Conference on Emergent Aspects in Clinical Data Analysis (2005)Google Scholar
  7. 7.
    López, A., Lloret, D., Serrat, J., Villanueva, J.: Multilocal creasness based on the level set extrinsic curvature. Computer Vision and Image Understanding 77, 111–144 (2000)CrossRefGoogle Scholar
  8. 8.
    Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vision and Computing 21(11), 977–1000 (2003)CrossRefGoogle Scholar
  9. 9.
    Ryan, N., Heneghan, C., de Chazal, P.: Registration of digital retinal images using landmark correspondence by expectation maximization. Image and Vision Computing 22, 883–898 (2004)CrossRefGoogle Scholar
  10. 10.
    Ortega, M., Penedo, M.G., Mariño, C., Carreira, M.J.: A novel similarity metric for retinal images based authentication. In: International Conference on Bio-inspired Systems and Signal Processing, vol. 1, pp. 249–253 (2009)Google Scholar
  11. 11.
    VARIA: Varpa retinal images for authentication, http://www.varpa.es/varia.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David Calvo
    • 1
  • Marcos Ortega
    • 1
  • Manuel G. Penedo
    • 1
  • José Rouco
    • 1
  • Beatriz Remeseiro
    • 1
  1. 1.VARPA Group, Department of Computer ScienceUniversity of A CoruñaSpain

Personalised recommendations