Toward more accurate iris recognition using dilated residual features

Iris recognition has emerged as the more accurate, convenient, and low-cost biometric approach to authenticate human subjects. However, the accuracy offered by current popular iris recognition algorithms is below the expectations from the community, and therefore, researchers have recently focused their attention on deep learning-based methods. This paper investigates a new deep learning-based approach for iris recognition and attempts to improve the accuracy using a more simplified framework to more accurately recover the representative features. We consider residual network learning with dilated convolutional kernels to optimize the training process and aggregate contextual information from the iris images. Such an approach also alleviates the need for the down-sampling and up-sampling layers, which not only results in a simplified network but also results in outperforming matching accuracy over several classical and state-of-the-art algorithms for iris recognition, i.e., further improvement in equal error rates by 7.14%, 10.7%, and 27.4% on three test databases. In this paper, our reproducible experimental results are presented on three publicly available datasets that illustrate outperforming results and validate the usefulness of our approach.