An iris recognition method is developed based on Log-Gabor filtering and feature fusion. Multichannel 2D Log-Gabor filters are employed to extract the iris features. To reduce redundancy of the Log-Gabor features

multiple Log-Gabor features in the same scale with different orientations are combined by using the magnitude information

and the fusion features are encoded based on the phase information.The similarity of two iris codes is measured by their weighted Hamming distance.The noise mask codes are adopted to reduce the interference of eyelids occlusion. In addition

a method for iris image quality assessment is presented

which can discriminate the images that are unsuitable for recognition.The approach can achieve lower equal error rate and lower false rejection rate under same false acceptance rate

and the size of the iris codes is only a half of the traditional approach. The experimental results show that the false rejection rates at the false acceptance rates of 0.01%and 0.1% are respectively decreased by 0.57% and 0.36%

the equal error rate is decreased by 0.25%

and the size of iris codes is decreased by 50%

compared to the results of the traditional Gabor approach.