Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
The lifting scheme: a construction of second generation wavelets
SIAM Journal on Mathematical Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine Real-Time Face Tracking Using a Wavelet Network
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Location of the Pupil-Iris Border in Slit-Lamp Images of the Cornea
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
On Techniques for Angle Compensation in Nonideal Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visibility of wavelet quantization noise
IEEE Transactions on Image Processing
Guest Editorial Applications Of Artificial Neural Networks To Image Processing
IEEE Transactions on Image Processing
Improving generalization performance using double backpropagation
IEEE Transactions on Neural Networks
Iris recognition based on elastic graph matching and Gabor wavelets
Computer Vision and Image Understanding
A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform
Pattern Recognition Letters
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One important category of non-ideal conditions for iris recognition is off-angle iris images. Practically it is very difficult for images to be captured with no offset. It then becomes necessary to account for off angle information in order to maintain robust performance. A biorthogonal wavelet based iris recognition system, previously designed at our lab, is modified and demonstrated to perform off-angle iris recognition. Biorthogonal wavelet network (BWN) are developed and trained for each class. The non-ideal factors are adjusted by repositioning the BWN. To test, along with the real data, synthetic iris images are generated by using affine and geometric transforms of 0^o, 10^o and 20^@? experimentally collected images. The tests were carried out on the experimentally collected off-angle data and synthetically generated data for angles from 0^o to 60^@? with a resolution of 5^@?. This approach is shown to have less constraints than a transformation based iris recognition approach. Iris images off-angle by up to 42^@? for synthetic data and up to 45^@? for experimental data are successfully recognized.