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Now let's looking back the gaze native
Seeing is essentially a perception, not a means
of action to make some effects in the world.
•It finds something to operate, and gets
continuous feedbacks of operation by other
means such as hand tactics etc.
Existing use cases of Gaze tracking in industry
• Control in shooting games
• Control of screen navigations
• Control of cursors or mouse pointer
• Analysis of user attentions and areas of interest
Marketing research or usability test
Existing use cases versus gaze native
functions, Leads to
Marketing research or
Matches with gaze
Let’s get back to the gaze native functions
Eyes are basically receptor and used as an effector
with someone else
(including pet animals) by
Showing interest on
something to someone else
(even animals use this eye
gesture to request
something to human)
Applications on top of gaze tracking should
take advantage of its native functions
• As a receptor: Finding
• As an effector: Showing interest of/with
Natural, No-fatigue, No-learning UX
As a receptor: Finding
• Tracking of the flow of
attention is more important
than the traditional point of
As an effector: Showing interest
of/with communication partner
• Environment SLAM used
together with gaze tracking is
more important than before.
• Many use cases would require
only rough estimation of gaze.
Hard to adopt for volume uses
High price, limited use cases, and low usability
Traditional Gaze tracker requires Infrared light sources and expensive
imaging device (zoom camera, two cameras, and so on).
Use case limitation and low usability
• Body attachment is bothering
• Low usability
Infrared light source
in head mounted
• Only for environment as Desktop PC
• It can capture gaze only in near distance (~1m)
• Suffers from outdoor light (eg, smart phone on the
road is under sun light)
Infrared light source
Gaze tracking by commodity camera
• They may use Face/Eye-Ball models and track iris to approximate the point of
• Low cost
• Unlike wearable type, users can have an open view
• Distance flexibility (a few meters)
• Free from environment with reasonable lighting condition
Challenge is the Accuracy
• Purple positions are also clearly captured under infrared light. The
infrared light source gives Purkinje reflections which give the
geometry of eye-balls and purples.
• On the other hand, the camera method rely on face/eye-ball models
and iris position tracking.
• It is not easy for commodity camera to track iris positions precisely.
• It is not easy for monocular camera to acquire precise Face/eye-ball 3D
It is bothering. It makes mass-adoption difficult.
Traditional gaze tracker requires initial setup of personal
parameters by asking user to see several known points.
• They may calibrate personal eye parameters such as Eye-ball center positions,
and Visual/optical axis delta
• They may use iterative algorithm to optimize parameters
• They may take advantages of user’s operation of object selection (mouse
click, touch etc) to calibrate parameters in the background
• Users are not aware of calibration process.
Challenges of Background calibration
Challenge: Smooth UX
• But UX
• Smooth transition
Challenge: Face, Eye-ball 3D Model
• Eye parameters
(eye ball center
delta) gives a
• Eye ball position in
face model and iris
gives a geometry.
It needs Face
besides that of
Challenge: Sensor-object calibration
• The attention
to camera position
• Imagine sensors in
a robot which can
move. Needs a
calibration of the
sensor and objects
Summary: Factors for Gaze tracking to be
among main-stream UIs
Natural use of
• Face 3D