Appearance-based gaze estimation has attracted more and more attention
because of its wide range of applications. The use of deep convolutional neural
networks has improved the accuracy significantly. In order to improve the
estimation accuracy further, we focus on extracting better features from eye
images. Relatively large changes in gaze angles may result in relatively small
changes in eye appearance. We argue that current architectures for gaze
estimation may not be able to capture such small changes, as they apply
multiple pooling layers or other downsampling layers so that the spatial
resolution of the high-level layers is reduced significantly. To evaluate
whether the use of features extracted at high resolution can benefit gaze
estimation, we adopt dilated-convolutions to extract high-level features
without reducing spatial resolution. In cross-subject experiments on the
Columbia Gaze dataset for eye contact detection and the MPIIGaze dataset for 3D
gaze vector regression, the resulting Dilated-Nets achieve significant (up to
20.8%) gains when compared to similar networks without dilated-convolutions.
Our proposed Dilated-Net achieves state-of-the-art results on both the Columbia
Gaze and the MPIIGaze datasets.

通过采用扩张卷积来提取高分辨率特征，以改善眼睛图像在显著变化下的精确度，我们提出了 Dilated-Net 模型，它在两个 Gaze 估计数据集上都取得了最先进的结果，相比不使用扩张卷积的类似网络获得了显著提高（高达 20.8%）。