Towards pervasive eye tracking using low-level image features

Zhang, Yanxia and Bulling, Andreas and Gellersen, Hans (2012) Towards pervasive eye tracking using low-level image features. In: Proceedings of the Symposium on Eye Tracking Research and Applications. ETRA '12 . ACM, New York, NY, USA, pp. 261-264. ISBN 978-1-4503-1221-9

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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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10 Aug 2012 18:05
Last Modified:
21 Nov 2022 14:45