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 selfsupervised 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 finetuned for any eye gaze estimation dataset.
