Abstract-In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local information and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms.
Ribeiro et al. REF proposed two deep learning structures for iris recognition: stacked auto-encoders (SAEs) and CNNs.
Exploring deep learning image super-resolution for iris recognition
2017 25th European Signal Processing Conference (EUSIPCO)
2017 25th European Signal Processing Conference (EUSIPCO)
Computer Science