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.

翻译：本文提出了一种用于任意分辨率虹膜识别的深度特征提取器。分辨率降低会削弱基于高分辨率图像训练的深度学习模型的识别性能。使用多分辨率图像进行训练虽能提升模型的鲁棒性，却会牺牲高分辨率图像的识别性能。为在不同分辨率下实现更高的识别性能，我们提出了一种具有自动切换网络功能的分辨率自适应特征提取方法。该框架包含专门针对不同分辨率退化（包括下采样和离焦模糊）设计的分辨率专家模块，并能根据输入图像的退化条件自动切换相应模块。低分辨率专家通过从高分辨率专家进行知识蒸馏的方式训练，使两类专家能提取共通的生物特征。我们将该框架应用于三种经典神经网络模型，实验结果表明：本方法在提升传统方法低分辨率识别性能的同时，完全保持了其高分辨率识别性能。