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%.
Chapter PDF
Similar content being viewed by others
References
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)
Tan, X., Bhanu, B.: A robust two step approach for fingerprint identification. Pattern Recognition Letters 24, 2127–2134 (2003)
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)
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)
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)
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)
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)
Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vision and Computing 21(11), 977–1000 (2003)
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)
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)
VARIA: Varpa retinal images for authentication, http://www.varpa.es/varia.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Calvo, D., Ortega, M., Penedo, M.G., Rouco, J., Remeseiro, B. (2009). Characterisation of Retinal Feature Points Applied to a Biometric System. In: Foggia, P., Sansone, C., Vento, M. (eds) Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science, vol 5716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04146-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-04146-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04145-7
Online ISBN: 978-3-642-04146-4
eBook Packages: Computer ScienceComputer Science (R0)