Iris is one of the most well-known biometrics; it is a nonintrusive and contactless authentication technique with high accuracy, enhanced security, and unique distinctiveness. However, its dependence on image quality and its frontal image acquisition requirement limit its recognition performance and hinder its potential use in standoff applications. Standoff biometric systems require a less controlled environment than traditional systems, so their captured images will likely be nonideal, including off-angle. We present convolutional neural network (CNN)-based deep learning frameworks to improve the recognition performance of iris, ocular, and periocular biometric modalities for off-angle images. Our contribution is fourfold: first, the performances of popular AlexNet, GoogLeNet, and ResNet50 architectures are presented for off-angle biometrics. Second, we study the effect of the gaze angle difference between training and testing images on iris, ocular, and periocular recognitions. Third, we investigate the network behavior for untrained gaze angles and the information fusion capability of CNN networks at multiple off-angle images. Finally, deep learning-based results are compared with a traditional iris recognition algorithm using the gallery approach. Our results with off-angle images ranging from −50 deg to 50 deg in gaze angle show that the proposed methods improve the recognition performance of iris, ocular, and periocular recognition.

中文翻译：

---

虹膜眼周：为偏角图像提供更准确的生物识别技术

虹膜是最著名的生物识别技术之一。它是一种非侵入式和非接触式身份验证技术，具有高精度、增强的安全性和独特的独特性。然而，它对图像质量的依赖和它的正面图像采集要求限制了它的识别性能并阻碍了它在对峙应用中的潜在用途。与传统系统相比，防区生物识别系统需要更少的控制环境，因此它们捕获的图像可能不理想，包括偏角。我们提出了基于卷积神经网络 (CNN) 的深度学习框架，以提高虹膜、眼部和眼周生物特征对偏角图像的识别性能。我们的贡献有四方面：首先，展示了流行的 AlexNet、GoogLeNet 和 ResNet50 架构的性能，用于偏角生物识别。其次，我们研究了训练和测试图像之间的注视角差异对虹膜、眼部和眼周识别的影响。第三，我们研究了未经训练的凝视角度的网络行为以及 CNN 网络在多个偏角图像上的信息融合能力。最后，将基于深度学习的结果与使用画廊方法的传统虹膜识别算法进行比较。我们对凝视角从 -50 度到 50 度的偏角图像的结果表明，所提出的方法提高了虹膜、眼部和眼周识别的识别性能。我们研究了未经训练的凝视角度的网络行为以及 CNN 网络在多个偏角图像上的信息融合能力。最后，将基于深度学习的结果与使用画廊方法的传统虹膜识别算法进行比较。我们对凝视角从 -50 度到 50 度的偏角图像的结果表明，所提出的方法提高了虹膜、眼部和眼周识别的识别性能。我们研究了未经训练的凝视角度的网络行为以及 CNN 网络在多个偏角图像上的信息融合能力。最后，将基于深度学习的结果与使用画廊方法的传统虹膜识别算法进行比较。我们对凝视角从 -50 度到 50 度的偏角图像的结果表明，所提出的方法提高了虹膜、眼部和眼周识别的识别性能。