Fuzzified Image Enhancement for Deep Learning in Iris Recognition

Abstract

Deep learning techniques such as convolutional neural network and capsule network have attained good results in iris recognition. However, due to the influence of eyelashes, skin, and background noises, the model often needs many iterations to retrieve informative iris patterns. Also because of some nonideal situations, such as reflection of glasses and facula on the eyeball, it is hard to detect the boundary of pupil and iris perfectly. Under such a circumstance, discarding the rest parts beyond the boundary may cause losing useful information. Hence, we use Gaussian, triangular fuzzy average, and triangular fuzzy median smoothing filters to preprocess the image by fuzzifying the region beyond the boundary to improve the signal-to-noise ratios. We applied the enhanced images through fuzzy operations to train deep learning methods, which speeds up the process of convergence and also increases the recognition accuracy rate. The saliency maps show that fuzzified image filters make the images more informative for deep learning. The proposed fuzzy operation of images may be a robust technique in many other deep-learning applications of image processing, analysis, and prediction.