Abstract:
Pupil dilations are sensitive to human affective responses. The
assessment of pupillary responses has found multi-purpose applications such
as lie detection in law enforcement agencies, providing an innovative solution
for human-computer interaction, minimizing dangers in air traffic displays,
communicating with people suffering from ailments such as autism, developing
interactive video games, eye gaze correction for videoconferencing, marketing
and consumer research.
In this work, the detection and assessment of pupil diameter is done by
developing a low-cost eye tracking system. The scope of the system
development is to accurately detect pupillary responses over a period of time
i.e. subtle changes in pupil size that indicate cognitive load. The features of the
proposed framework are eye and pupil-localization, pupil area and diameter
calculation in a recorded video sequence and investigation of pupil size
variation during and after auditory stimulation. In this case human eye images
were acquired by a webcam and processed offline.
The hardware includes a micro-lens webcam and head mounted
structure with a USB data transfer capability to computer. The framework for
the algorithm development includes iris and pupil localization, pupil area and
diameter extraction and calibration that are implemented in MATLAB.
Subsequently, the algorithm is implemented on video sequences from the
webcam. Pattern Recognition techniques are used to transform the images
from RGB to binary and Hough Transform is used to detect pupil area from the
rest of the eye image. Moreover, a high performance implementation of the
proposed algorithm is done using MATLAB® parallel processing toolbox and
Graphical Processor Unit (GPU). A comparison of speedups achieved by
implementing the above techniques is analyzed. Finally, experiments are
conducted on two subjects to measure human cognition and the algorithmic
efficiency of eye pupil location and variation.
