  Pupil reflex to variations in illumination and associated dynamics are of
importance in neurology and ophthalmology. This is typically measured using a
near Infrared (IR) pupillometer to avoid Purkinje reflections that appear when
strong Visible Light (VL) illumination is present. Previously we demonstrated
the use of deep learning techniques to accurately detect the pupil pixels
(segmentation binary mask) in case of VL images for performing VL pupillometry.
Here, we present a method to obtain the parameters of the elliptical pupil
boundary along with the segmentation binary pupil mask. This eliminates the
need for an additional, computationally expensive post-processing step of
ellipse fitting and also improves segmentation accuracy. Using the time-varying
ellipse parameters of pupil, we can compute the dynamics of the Pupillary Light
Reflex (PLR). We also present preliminary evaluations of our deep learning
algorithms on experimental data. This work is a significant push in our goal to
develop and validate a VL pupillometer based on a smartphone that can be used
in the field.
