Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35615
Title: Building a self-learning eye gaze model from user interaction data
Authors: Huang, MX
Kwok, TCK
Ngai, G 
Leong, HV 
Chan, SCF 
Keywords: Data validation
Gaze estimation
Implicit modeling
Supervised learning
Issue Date: 2014
Publisher: Association for Computing Machinery, Inc
Source: MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia, 2014, p. 1017-1020 How to cite?
Abstract: Most eye gaze estimation systems rely on explicit calibration, which is inconvenient to the user, limits the amount of possible training data and consequently the performance. Since there is likely a strong correlation between gaze and interaction cues, such as cursor and caret locations, a supervised learning algorithm can learn the complex mapping between gaze features and the gaze point by training on incremental data collected implicitly from normal computer interactions. We develop a set of robust geometric gaze features and a corresponding data validation mechanism that identifies good training data from noisy interaction-informed data collected in real-use scenarios. Based on a study of gaze movement patterns, we apply behavior-informed validation to extract gaze features that correspond with the interaction cue, and data-driven validation provides another level of crosschecking using previous good data. Experimental evaluation shows that the proposed method achieves an average error of 4.06°, and demonstrates the effectiveness of the proposed gaze estimation method and corresponding validation mechanism.
Description: 2014 ACM Conference on Multimedia, MM 2014, 3-7 November 2014
URI: http://hdl.handle.net/10397/35615
ISBN: 9781450330633
DOI: 10.1145/2647868.2655031
Appears in Collections:Conference Paper

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