Abstract
In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as “cognition” one class by one class, and it doesn’t influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boles, W., Boashash, B.: A Human Identification Technique Using Image of the Iris and Wavelet Transform. IEEE Trans. Signal Process 46, 1185–1188 (1998)
Wang, S., Li, Z., Chen, X., Wang, B.: Discussion on the Basic Mata-hematical Models of Neurons in General Purpose Neurocomputer. Acta Electronica Sinica 29, 577–580 (2001)
Wang, S.: A New Development on ANN in China - Biomimetic Pattern Recognition and Multi weight Vector Neurons. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 35–43. Springer, Heidelberg (2003)
Yang, W., Yu, L., et al.: A Fast Iris Location Algorithm. Computer Engineering and Applications 10 (2004)
Wang, Y., Zhu, Y., Tan, T.: Biometrics Personal Identification Based on Iris Pattern. Acta Automatica Sinica 28, 1–10 (2002)
Li, M., et al.: Local Intensity Variation Analysis for Iris Recognition. Pattern Recognition 37, 1287–1298 (2004)
Han, F., Chen, Y., Lu, H.: An Effective Iris Location Algorithm. Journal of Shanghai University (Natural Science) 7, 1–3 (2001)
Cao, W., Feng, H., Wang, S.: The application of DBF Neural Networks for Object Recognition. Inf. Sci. 160, 153–160 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, W., Hu, J., Xiao, G., Wang, S. (2005). Application of Multi-weighted Neuron for Iris Recognition. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_15
Download citation
DOI: https://doi.org/10.1007/11427445_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)