Abstract Comprehensive and accurate eye modelling is crucial to a variety of applications, including human-computer interaction, assistive technologies, and medical diagnosis. However, most studies focus on the localization of one or two components of eyes, such as pupil or iris, lacking a comprehensive eye model. We propose to model an eye image by a set of parametric curves. The set of curves are plotted on an eye image to form a Contour-Eye image. A deep neural network is trained to evaluate the fitness of the Contour-Eye image. Then an evolutionary process is conducted to search the best fitting curve set, guided by the trained deep neural network. Finally, an accurate eye model with optimized parametric curves is obtained. For the algorithm evaluation, a finely annotated eye dataset denoted as FAED-50 is established by us, which contains 2,498 eye images from 50 subjects. The experimental results on the FAED-50 and the relabeled CASIA datasets and comparison with the state-of-the-art methods demonstrate the effectiveness and accuracy of the proposed parametric model.

中文翻译：

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面向深度神经网络的进化参数眼建模

摘要 全面准确的眼部建模对于包括人机交互、辅助技术和医疗诊断在内的各种应用至关重要。然而，大多数研究都集中在眼睛的一两个组件的定位上，例如瞳孔或虹膜，缺乏全面的眼睛模型。我们建议通过一组参数曲线对眼睛图像进行建模。这组曲线绘制在眼睛图像上以形成轮廓眼图像。训练深度神经网络来评估 Contour-Eye 图像的适应度。然后在经过训练的深度神经网络的指导下，进行进化过程以搜索最佳拟合曲线集。最后，获得具有优化参数曲线的精确眼部模型。对于算法评估，我们建立了一个标记为 FAED-50 的精细注释的眼睛数据集，其中包含 2 个，来自 50 个对象的 498 个眼睛图像。FAED-50 和重新标记的 CASIA 数据集的实验结果以及与最先进方法的比较证明了所提出的参数模型的有效性和准确性。