Learning aspects of interest from Gaze

Authors:
Kei Shimonishi;Hiroaki Kawashima;Ryo Yonetani;Erina Ishikawa;Takashi Matsuyama
Affiliations:
Kyoto University, Kyoto, Japan;Kyoto University, Kyoto, Japan;Kyoto University, Kyoto, Japan;Kyoto University, Kyoto, Japan;Kyoto University, Kyoto, Japan
Venue:
Proceedings of the 6th workshop on Eye gaze in intelligent human machine interaction: gaze in multimodal interaction
Year:
2013

Citing 4
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Abstract

This paper presents a probabilistic framework to model the gaze generative process when a user is browsing a content consisting of multiple regions. The model enables us to learn multiple aspects of interest from gaze data, to represent and estimate user's interest as a mixture of aspects, and to predict gaze behavior in a unified framework. We recorded gaze data of subjects when they were browsing a digital pictorial book, and confirmed the effectiveness of the proposed model in terms of predicting the gaze target.