GLASE 0.1: eyes tell more than mice

Authors:
Viktors Garkavijs;Mayumi Toshima;Noriko Kando
Affiliations:
The Graduate University for Advanced Sciences, Tokyo, Japan;The Graduate University for Advanced Sciences, Tokyo, Japan;National Institute of Informatics, Tokyo, Japan
Venue:
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Year:
2012

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Abstract

This paper proposes a prototype system called Gaze-Learning-Access-and-Search-Engine 0.1 (GLASE), which can perform image relevance ranking based on gaze data and within-session learning. We developed a search user interface that uses an eye-tracker as an input device and employed a relevance re-ranking algorithm based on the gaze length. The preliminary experimental results showed that using our gaze-driven system reduced the task completion time an average of 13.7% in a search session.