Application of Iris Images in Racial Classifications Based on Dilate Convolution and Residual Network

Abstract

The iris is an area of the approximation of the ring between the pupil and the sclera of the human eye, which contains a large number of texture features. Due to the stability and specificity of the iris texture features, the iris can solve problems such as racial classification. The existing method of racial classification by iris image mainly adopts manual extraction of features and classification research, which has certain limitations. We proposed a method based on improved residual network for iris image race classification. In order to more fully extract the iris image features, we divide the network into two parts. We take apart in the first part of the network to the channel, for each channel for unused convolution kernels is utilized to extract features, and then connect the second part the back-end network residual, need special pointed out that in order to increase the receptive field. We use dilate convolution in each convolution layer, and we also use CAIAS and UBIRIS public data sets to verify the effectiveness of our method, the classification accuracy is 96.71%, and F1-score is 0.97. It is a good way to realize the basic classification of species and subspecies, improves the precision and feasibility of racial classifications, makes the racial classification theory more perfect.