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Conference Paper

Towards Pervasive Gaze Tracking with Low-level Image Features

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Yanxia, Z., Bulling, A., & Gellersen, H. (2012). Towards Pervasive Gaze Tracking with Low-level Image Features. In S. N. Spencer (Ed.), Proceedings ETRA 2012 (pp. 261-264). New York, NY: ACM.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0017-9BDD-9
We contribute a novel gaze estimation technique, which is adaptable for person-independent applications. In a study with 17 participants, using a standard webcam, we recorded the subjects\textquoteright} left eye images for different gaze locations. From these images, we extracted five types of basic visual features. We then sub-selected a set of features with minimum Redundancy Maximum Relevance (mRMR) for the input of a 2-layer regression neural network for estimating the subjects{\textquoteright} gaze. We investigated the effect of different visual features on the accuracy of gaze estimation. Using machine learning techniques, by combing different features, we achieved average gaze estimation error of 3.44{\textdegree} horizontally and 1.37{\textdegree vertically for person-dependent.