  This paper proposes a deep feature extractor for iris recognition at
arbitrary resolutions. Resolution degradation reduces the recognition
performance of deep learning models trained by high-resolution images. Using
various-resolution images for training can improve the model's robustness while
sacrificing recognition performance for high-resolution images. To achieve
higher recognition performance at various resolutions, we propose a method of
resolution-adaptive feature extraction with automatically switching networks.
Our framework includes resolution expert modules specialized for different
resolution degradations, including down-sampling and out-of-focus blurring. The
framework automatically switches them depending on the degradation condition of
an input image. Lower-resolution experts are trained by knowledge-distillation
from the high-resolution expert in such a manner that both experts can extract
common identity features. We applied our framework to three conventional neural
network models. The experimental results show that our method enhances the
recognition performance at low-resolution in the conventional methods and also
maintains their performance at high-resolution.
