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Towards pervasive eye tracking using low-level image features

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date2012
Host publicationProceedings of the Symposium on Eye Tracking Research and Applications
Place of publicationNew York, NY, USA
PublisherACM
Pages261-264
Number of pages4
ISBN (Print)978-1-4503-1221-9
Original languageEnglish

Publication series

NameETRA '12
PublisherACM

Abstract

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' 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' 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° horizontally and 1.37° vertically for person-dependent.

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