Abstract
Automatic eye gaze estimation has interested researchers for a while now. In this paper, we propose an unsupervised learning based method for estimating the eye gaze region. To train the proposed network "Ize-Net" in self-supervised manner, we collect a large `in the wild' dataset containing 1,54,251 images from the web. For the images in the database, we divide the gaze into three regions based on an automatic technique based on pupil-centers localization and then use a feature-based technique to determine the gaze region. The performance is evaluated on the Tablet Gaze and CAVE datasets by fine-tuning results of Ize-Net for the task of eye gaze estimation. The feature representation learned is also used to train traditional machine learning algorithms for eye gaze estimation. The results demonstrate that the proposed method learns a rich data representation, which can be efficiently fine-tuned for any eye gaze estimation dataset.
Abstract (translated)
目前，自动眼睛注视估计已经引起了研究人员的兴趣。本文提出了一种基于无监督学习的眼睛注视区域估计方法。为了以自我监督的方式训练提议的网络“ize net”，我们从网络上收集了一个包含1,54251个图像的大型“在野外”数据集。对于数据库中的图像，我们基于基于瞳孔中心定位的自动技术将注视分成三个区域，然后利用基于特征的技术确定注视区域。通过对IZE网用于眼睛注视估计任务的微调结果，对平板注视和洞穴数据集的性能进行了评价。所学的特征表示也用于训练传统的机器学习算法，用于眼睛注视估计。结果表明，该方法学习到了丰富的数据表示，可以有效地对任何眼睛注视估计数据集进行微调。
URL
https://arxiv.org/abs/1904.02459
