Abstract
Iris recognition has been paid more attentions due to its high reliability in personal identification recently. Iris feature extraction is very critical in the identification system. In this paper, in order to obtain the effective iris feature matrices with lower dimension, we explore a feature extraction method called Complete Two-Dimension Principal Component Analysis (C- 2DPCA). We also employed other two methods, Two-Dimension Linear Discriminant Analysis (2DLDA) and 2DPCA for comparison. Experiments with the public iris dataset from Chinese Academy of Science - Institute of Automation (CASIA) indicate that the C-2DPCA performs better than both 2DLDA and 2DPCA with a lower Equal Error Rate (EER) and average computation time.
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Xu, X., Guo, P. (2009). Iris Feature Extraction Based on the Complete 2DPCA. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_108
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DOI: https://doi.org/10.1007/978-3-642-01510-6_108
Publisher Name: Springer, Berlin, Heidelberg
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