Iris Feature Extraction Based on the Complete 2DPCA

Authors:
Xiuli Xu;Ping Guo
Affiliations:
Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing, China 100081 and School of Computer Science and Technology, Beijing Institute of Technology, Bei ...;School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China 100081
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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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.