With the rapid development of deep learning, iris recognition methods based on deep learning are constantly being proposed. These methods generally consist of iris segmentation and normalization to more accurately locate iris regions and reduce the impact of iris feature changes caused by pupil expansion. We propose a non-segmentation (NS) iris recognition framework based on a deep learning classification model, which directly takes a raw image as input for feature extraction and recognition without performing iris segmentation and normalization. This method outperforms other methods. We proposed a non-segmentation network (NSNet). NSNet is a convolutional neural network (CNN) based on an attention mechanism that enhances the robustness and accuracy of the network by reusing features and assigning channel attention values. In addition, while ensuring the advanced performance of the network, it uses only 31 convolutional layers to complete iris feature extraction and recognition tasks in the NS iris recognition framework. Since the deep learning classification model cannot recognize that the category of an image is an untrained image category, we proposed a dual-threshold iris framework. In the proposed framework, all untrained image categories are classified as impostor classes, and the first threshold set in the proposed framework can effectively prevent impostors and eliminate inferior images. The proposed framework is suitable for the pursuit of more accurate, more robust, and safer private iris recognition scenarios. We conduct experiments with uniform parameter settings on four publicly available databases and shows that even in the challenging situation where a raw image is used as the input image, the proposed method is still the most advanced algorithm. Moreover, a series of ablation experiments were conducted to further confirm that the proposed framework has a higher accuracy, robustness, and generalizability through verification and discussion.

中文翻译：

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非细分框架可实现准确而强大的虹膜识别

随着深度学习的飞速发展，基于深度学习的虹膜识别方法不断被提出。这些方法通常由虹膜分割和归一化组成，以更准确地定位虹膜区域并减少由瞳孔扩展引起的虹膜特征变化的影响。我们提出了一种基于深度学习分类模型的非细分（NS）虹膜识别框架，该框架直接将原始图像作为输入进行特征提取和识别，而无需执行虹膜分割和归一化。此方法优于其他方法。我们提出了一个非分段网络（NSNet）。NSNet是基于注意力机制的卷积神经网络（CNN），可通过重用特征和分配通道注意力值来增强网络的鲁棒性和准确性。此外，在确保网络高级性能的同时，它仅使用31个卷积层即可完成NS虹膜识别框架中的虹膜特征提取和识别任务。由于深度学习分类模型无法识别图像的类别是未经训练的图像类别，因此我们提出了双阈值虹膜框架。在提出的框架中，所有未经训练的图像类别都被归类为冒名顶替者类别，并且在提出的框架中设置的第一阈值可以有效地防止冒名顶替者并消除劣质图像。提出的框架适合于追求更准确，更健壮和更安全的私人虹膜识别方案。我们在四个公开可用的数据库上使用统一的参数设置进行了实验，结果表明，即使在原始图像用作输入图像的挑战性情况下，所提出的方法仍然是最先进的算法。此外，进行了一系列的消融实验，通过验证和讨论进一步证实了所提出的框架具有更高的准确性，鲁棒性和通用性。