CNN-Based Gaze Estimation for Off-angle Iris Recognition
CNN-Based Gaze Estimation for Off-angle Iris Recognition
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DOI：
10.1109/southeastcon48659.2022.9763926
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Khalid Diab;M. Karakaya
中科院分区：
文献类型：
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作者：
Khalid Diab;M. Karakaya
远距离虹膜识别系统不仅可以识别完全合作的个体，还可以识别非合作的运动物体。由于虹膜图像是在限制条件较少的情况下拍摄的，因此它们很可能是不理想的，包括偏离角度。然而，传统的虹膜识别系统只适用于正面虹膜图像。因此，现有的数据库仅使用正面图像来保存用于虹膜匹配的二进制虹膜代码。为了使用这些现有的虹膜编码，对准系统需要与现有的虹膜匹配算法兼容，即使对于偏角图像，也需要生成正面版本的虹膜编码。由于视线估计是重建离角虹膜图像正视图的第一个必不可少的组成部分，因此我们首先需要从单个离角虹膜图像估计眼睛的凝视方向。提出了一种基于深度学习的远距离虹膜识别框架的视角估计方法。作为主要贡献，我们的方法将允许我们使用转移学习和回归模型来估计离角度图像的凝视角度，而不需要分割和使用额外的硬件。本文提出的基于CNN的视线估计方法以100名被试的108,600张偏角图像为样本进行训练和测试。在训练图像不是所有角度都可用的情况下，对于-50°到+50°范围内的偏角图像，该算法具有较高的凝视估计精度，平均角度误差为4°。
Standoff iris recognition systems have been developed to identify not only fully cooperative individuals but also non-cooperative moving subjects. Since iris images are captured in much less constrained conditions, they are likely to be non-ideal including being off-angle. However, traditional iris recognition systems are designed for only frontal iris images. Therefore, the existing databases save binary iris codes using only frontal images for iris matching. To use these existing iris codes, standoff systems need to be compatible with existing iris matching algorithms by generating the frontal version of iris codes even for off-angle images. Since gaze estimation is the first essential component to reconstruct the frontal view of off-angle iris images, we first need to estimate the eye gaze direction from a single off-angle iris image. This paper presents a deep learning-based gaze angle estimation method for standoff iris recognition frameworks. As the main contribution, our approach will allow us to estimate the gaze angle from an off-angle image using transfer learning and a regression model without segmentation and using additional hardware. The proposed CNN-based gaze estimation method is trained and tested with 108,600 off-angle images from 100 subjects. It shows high accuracy in gaze estimation with an average error of 4° in angle for off-angle images captured from every angle ranging -50° to +50° even if training images are not available for all angles.