Abstract
In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. 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 rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.
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Cao, W., Hu, J., Xiao, G., Wang, S. (2005). Iris Recognition Algorithm Based on Point Covering of High-Dimensional Space and Neural Network. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_30
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DOI: https://doi.org/10.1007/11510888_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
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