  Eye movements play a vital role in perceiving the world. Eye gaze can give a
direct indication of the users point of attention, which can be useful in
improving human-computer interaction. Gaze estimation in a non-intrusive manner
can make human-computer interaction more natural. Eye tracking can be used for
several applications such as fatigue detection, biometric authentication,
disease diagnosis, activity recognition, alertness level estimation,
gaze-contingent display, human-computer interaction, etc. Even though
eye-tracking technology has been around for many decades, it has not found much
use in consumer applications. The main reasons are the high cost of eye
tracking hardware and lack of consumer level applications. In this work, we
attempt to address these two issues. In the first part of this work,
image-based algorithms are developed for gaze tracking which includes a new
two-stage iris center localization algorithm. We have developed a new algorithm
which works in challenging conditions such as motion blur, glint, and varying
illumination levels. A person independent gaze direction classification
framework using a convolutional neural network is also developed which
eliminates the requirement of user-specific calibration.
  In the second part of this work, we have developed two applications which can
benefit from eye tracking data. A new framework for biometric identification
based on eye movement parameters is developed. A framework for activity
recognition, using gaze data from a head-mounted eye tracker is also developed.
The information from gaze data, ego-motion, and visual features are integrated
to classify the activities.
