Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis and Improvement of An Iris Identification Algorithm
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Iris Recognition Algorithm Using Modified Log-Gabor Filters
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comments on the CASIA version 1.0 Iris Data Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 using independent component analysis
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
Iris Matching by Local Extremum Points of Multiscale Taylor Expansion
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Iris recognition by fusing different representations of multi-scale Taylor expansion
Computer Vision and Image Understanding
A new cow identification system based on iris analysis and recognition
International Journal of Biometrics
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Rich texture of iris image allows to perform iris-based person authentication with very high confidence. We propose to use the most significant local extrema of the first two Taylor expansion coefficients as descriptors of the iris texture. The proposed features can be efficiently compared and even can correct moderate inaccuracies in iris segmentation during the matching stage. A brief introduction of our iris segmentation algorithm is followed by the analysis of the proposed features and the similarity function. We provide experimental results of verification quality for three commonly used iris data sets. An analysis of strong and weak aspects of the proposed approach is done.