Is normalized iris optimal for iris recognition based on deep learning?

The input of the iris classification network based on deep learning has two forms: one is the coarsely located iris region of interest; the other is the normalized iris. To solve the problem of whether it is necessary to normalize the iris, experiments are carried out on the above two input forms, and the results show that the iris normalization is still the best choice. To adapt to the visual characteristics of the neural network, an iris normalization processing method is proposed: starting from 90 deg, the iris circle is mapped to polar coordinates and the normalized rectangle is cropped, rotated, and spliced. Experimental and visualization results show that the proposed iris normalization strategy has better results of iris recognition than other normalization methods. (C) 2021 SPIE and IS&T