High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Functional dissipation microarrays for classification
Pattern Recognition
Iris recognition based on statistical assessment of wavelet coefficients
International Journal of Computer Applications in Technology
An efficient iris recognition system based on modular neural networks
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
ISA '09 Proceedings of the 3rd International Conference and Workshops on Advances in Information Security and Assurance
IEEE Transactions on Image Processing
Iris recognition based on non-local comparisons
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
A new feature extraction method using the ICA filters for iris recognition system
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
An iris detection method based on structure information
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
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In general, the iris recognition systems have used the wavelet transform as feature extraction techniques. Since the wavelet transform does not have the shift-invariant property, the iris features are inconsistently extracted due to the eye image rotation and the inexact iris localization. In this paper, a novel method to extract features is proposed for iris recognition system. Two types of features are obtained from the discrete wavelet frame decomposition. The first one is the global feature which is insensitive to the iris image deformation. The second one is the local feature which can represent the iris local texture. If the global distance between the test image and the stored one in the database is smaller than the threshold value, it is added to the candidates. And then, local matching is performed by Hamming distance. Experimental results show the proposed system could be used for the personal recognition efficiently.