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Gaze Estimation for Off-Angle Iris Recognition Based on the Biometric Eye Model

机译：基于生物特征眼模型的偏角虹膜识别注视估计

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摘要

Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-angle iris recognition framework based on the ORNL biometric eye model. Gaze estimation is an important prerequisite step to correct an off-angle iris images. To achieve the accurate frontal reconstruction of an off-angle iris image, we first need to estimate the eye gaze direction from elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.
机译：虹膜识别是最高精度的生物识别技术之一。但是，其准确性依赖于受控的高质量捕获数据，并受到诸如角度，遮挡和扩张等多种因素的负面影响。非理想虹膜识别是生物识别技术的新研究重点。在本文中，我们提出了一种凝视估计方法，该方法设计用于基于ORNL生物特征识别眼模型的斜角虹膜识别框架。注视估计是校正偏角虹膜图像的重要先决条件步骤。为了实现斜角虹膜图像的准确的正面重建，我们首先需要从虹膜图像的椭圆形特征估计眼睛注视方向。通常，其他信息（如受控光源，头戴式设备和多个摄像头）不可用。我们的方法仅利用虹膜和瞳孔边界分割，使其适用于所有虹膜捕获硬件。我们将边界与使用生物学启发的生物特征识别眼睛模型生成的查找表进行比较，并在查找表中找到最接近的特征点以估计视线。基于真实图像的结果，所提出的方法显示出对我们的生物识别眼模型的凝视估计精度的有效性，在50度范围内的平均误差约为3.5度。

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