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Pupil reflex to variations in illumination and associated dynamics are ofimportance in neurology and ophthalmology. This is typically measured using anear Infrared (IR) pupillometer to avoid Purkinje reflections that appear whenstrong Visible Light (VL) illumination is present. Previously we demonstratedthe 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 pupilboundary along with the segmentation binary pupil mask. This eliminates theneed for an additional, computationally expensive post-processing step ofellipse fitting and also improves segmentation accuracy. Using the time-varyingellipse parameters of pupil, we can compute the dynamics of the Pupillary LightReflex (PLR). We also present preliminary evaluations of our deep learningalgorithms on experimental data. This work is a significant push in our goal todevelop and validate a VL pupillometer based on a smartphone that can be usedin the field.

Direct Estimation of Pupil Parameters Using Deep Learning for Visible Light Pupillometry