Iris recognition is one of the most promising fields in biometrics due to more accurate, convenient and low-cost. However, it is still a challenging task for application in practical complex scenarios. More attention have been paid on non-ideal iris segmentation and cross-system feature extraction in recent years. In order to solve the issues, this paper investigates a novel non-normalized preprocessing method based on dynamic path search for iris segmentation. Meanwhile, we employ a deep convolution network (DCNN) based on partial convolution operators to extract iris features. Through benchmark experiments on two public iris datasets CASIA-Iris-Thousand (CASIA) and IIT Delhi Iris Dataset (IITD), we achieve the significant and encouraging results, which demonstrate the effectiveness of the proposed methods. More importantly, we prove that using iris segmentation images without normalization may be a better choice when exploring iris recognition solutions based on deep learning.

虹膜识别由于更准确，方便和低成本而成为生物识别领域最有前途的领域之一。但是，在实际的复杂场景中应用仍然是一项艰巨的任务。近年来，非理想虹膜分割和跨系统特征提取已引起更多关注。为了解决这一问题，本文研究了一种基于动态路径搜索的虹膜分割的非标准化预处理方法。同时，我们采用基于部分卷积算子的深度卷积网络（DCNN）提取虹膜特征。通过对两个公共虹膜数据集CASIA-Iris-Thousand（CASIA）和IIT Delhi Iris数据集（IITD）进行基准实验，我们获得了令人鼓舞的令人鼓舞的结果，证明了所提出方法的有效性。更重要的是，