Abstract:
The aim of this thesis is to explore new applications in the area of human computer
interaction and to propose solution for these applications based upon gaze direction and
head gesture. Gaze direction and head gesture are considered as input modalities for
human computer interaction with different degree of freedom and different capabilities.
Gaze direction estimation is achieved by subsequent stages: face detection, eye detection,
eye gaze estimation and coordinate mapping for interaction of gaze over natural world
surface. Face detection has been achieved by adaboost which combine visual critical
feature based weak learner and produce a strong classifier. Assumingly face is detected,
and then eyes are detected based upon texture feature. A regression neural network based
gaze interaction with a surface is proposed. The regression neural network is trained over
eye image while gazing in several directions. Accuracy of the proposed system is based
upon the performance of this regression neural network that has to produce the
coordinate which are being gazed by human eye. The detected eye gaze is further
correlated with head gesture: head shake and head node to provide interaction
mechanism with the real world. Practical performance of the system was tested in
different real world environment such as infotainment device control and in automotive.
The dissertation also proposes two novel applications in the area of augmented reality
based upon gaze direction and head gesture. Augmented reality is combination of real
viworld and computer generated data. A subset of gaze direction is proposed in which head
orientation is considered and gross level gaze direction is proposed. This gross level gaze
defines current field of view which is then animated and useful information is displayed
for situation aware environments.
