Automatic eye gaze estimation using geometric texture-based networks

Shreyank Jyoti, Abhinav Dhall

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


Eye gaze estimation is an important problem in automatic human behavior understanding. This paper proposes a deep learning based method for inferring the eye gaze direction. The method is based on the use of ensemble of networks, which capture both the geometric and texture information. Firstly, a Deep Neural Network (DNN) is trained using the geometric features that are extracted from the facial landmark locations. Secondly, for the texture based features, three Convolutional Neural Networks (CNN) are trained i.e. For the patch around the left eye, right eye, and the combined eyes, respectively. Finally, the information from the four channels is fused with concatenation and dense layers are trained to predict the final eye gaze. The experiments are performed on the two publicly available datasets: Columbia eye gaze and TabletGaze. The extensive evaluation shows the superior performance of the proposed framework. We also evaluate the performance of the recently proposed swish activation function as compared to Rectified Linear Unit (ReLU) for eye gaze estimation.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition (ICPR)
Subtitle of host publicationAug. 20 2018 to Aug. 24 2018 Beijing, China
EditorsCheng-Lin Liu, Rama Chellappa, Matti Pietikäinen
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538637883, 9781538637876
ISBN (Print)9781538637890
Publication statusPublished - 2018
Externally publishedYes
EventInternational Conference on Pattern Recognition 2018 - Beijing, China
Duration: 20 Aug 201824 Aug 2018
Conference number: 24th


ConferenceInternational Conference on Pattern Recognition 2018
Abbreviated titleICPR 2018
Internet address

Cite this