High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
Texture classification with a biorthogonal directional filter bank
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Iris-based personal authentication using a normalized directional energy feature
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Iris feature extraction and matching based on multiscale and directional image representation
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
Improved structures of maximally decimated directional filter Banks for spatial image analysis
IEEE Transactions on Image Processing
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Using fragile bit coincidence to improve iris recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
On combining selective best bits of iris-codes
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
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In this paper, we deal with extracting and combining multimodal iris features for person verification. In multibiometric approaches, finding reasonably disjoint features and effective combining methods are crucial. The proposed method considers the directional characteristics of iris patterns as critical features, and first decomposes an iris image into several directional subbands using a directional filter bank (DFB), then generates two kinds of feature vectors from the directional subbands. One is the binarized output features of the directional subbands on multiple scales and the other is the blockwise directional energy features. The former is relatively robust to changes in illumination or image contrast because it uses the directional zero crossing information of the directional subbands, whereas the latter provides another form of rich directional information though it is a bit sensitive to contrast change. Matching is performed separately between the same kind of feature vectors and the final decision is made by combining the matching scores based on the accuracy of each method. Experimental results show that the two kinds of feature vectors used in this paper are reasonably complementary and the combining method is effective.