A novel biorthogonal wavelet network system for off-angle iris recognition

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°, 10° 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° 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.