Abstract:
Gaze estimation and tracking systems largely depend on head-mounted cameras
which make them cumbersome to use, thus, they are restricted to laboratory set up. In
recent studies, researchers have attempted to estimate gaze using mouse cursor which
has produced inaccurate results because factors that influence eye-gaze region have
not been considered. This thesis examined existing methods for gaze estimation then
proposed a framework for estimating gaze region using enhanced cursor based
trackers. The work investigated mouse activities, keyboard activities and document
scrolls activities on web pages which together were used to estimate gaze region in
web page documents. The main tool used this study for data collection was a web
application with client-side code for recording mouse, keyboard activities and
document scrolls while users navigate through web page documents. Data collected
was pushed to remote web server for storage and analysis. Analysis involved two
stages. Stage one involved extraction of scroll activities from mouse and keyboard
activities because scroll can be achieved by either of these. Stage two involved use of
Lindeman, Merenda, and Gold (LMG) metric linear regression model to estimate
gaze from mouse, keyboard and scroll data. The main outcome of this work was a
framework for estimating users gaze. To achieve this, we first came up with a
theoretical model describing relationship between mouse activities, keyboard
activities, document scrolls activities and the user gaze. An algorithm that uses LMG
metric linear regression model was then used to estimate users region from collection
mouse, keyboard and scroll activities data. The results shows that mouse activities
only cannot be relied on to estimate user’s gaze. Both keyboard and scroll activities
are play a significant role in gaze over a period of time and must therefore be
considered when estimating user’s gaze region.
