TY - GEN
T1 - Unsupervised Learning of Eye Gaze Representation from the Web
AU - Dubey, Neeru
AU - Ghosh, Shreya
AU - Dhall, Abhinav
PY - 2019/9/30
Y1 - 2019/9/30
N2 - 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 finetuned for any eye gaze estimation dataset.
AB - 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 finetuned for any eye gaze estimation dataset.
UR - http://www.scopus.com/inward/record.url?scp=85073227265&partnerID=8YFLogxK
UR - https://www.proceedings.com/content/050/050604webtoc.pdf
U2 - 10.1109/IJCNN.2019.8851961
DO - 10.1109/IJCNN.2019.8851961
M3 - Conference contribution
AN - SCOPUS:85073227265
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 3488
EP - 3494
BT - 2019 International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers
T2 - 2019 International Joint Conference on Neural Networks
Y2 - 14 July 2019 through 19 July 2019
ER -