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《工程(英文)》 >> 2021年 第7卷 第6期 doi: 10.1016/j.eng.2020.08.027

基于参考网格的差分眼部外观网络的视线估计

a Chengdu Aeronautic Polytechnic, Chengdu 610100, China
b Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden

收稿日期: 2019-11-08 修回日期: 2020-06-11 录用日期: 2020-08-06 发布日期: 2021-04-30

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摘要

人类的视线可以有效地传递人们的意图,因此,视线估计方法是智能制造中意图传递的重要研究内容。很多方法通过分析眼部图像,称为眼部图像片,实现视线方向的回归运算。但是,由于眼部图像存在个体差异,这类方法很难建立一个样本无关模型进行视线估计。在本文中,作者假设人眼的外观差异与视线方向差异有直接联系。基于这个假设,本文利用双眼眼部图像片在不同视线时的图像差异估计相应两种视线的差值,构建了差分眼部外观网络(differential eyes’ appearances network, DEANet),并在公共数据集中进行训练。本文提出的DEANet主要基于孪生神经网络(Siamese neural network, SNNet)构建,包含两个结构相同的网络分支。多流数据分别输入到此孪生神经网络的两个分支中。两个网络分支共享相同的权值,实现眼部图像片的特征提取,然后对特征进行拼接,从而获得视线方向的差异。只要完成了视线方向差异模型的训练,在少量的校准图像片的情况下,就可以对其他样本的视线方向差异进行估计。由于测试阶段包含了被测试者的眼部信息,因此估计精度进一步提高。此外,本文提出的方法还有效地避免了在训练样本相关模型时需要大量数据的问题。本文还提出了一种参考网格策略,以便在测试阶段有效地选择一些参考眼部图像片,将它们作为网络的一部分输入,从而进一步提高估计精度。在公共数据集上的实验表明,本文提出的方法优于当前的方法。

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