Automatic Pupillary Light Reflex Detection in Eyewear Computing

There are many benefits to facilitating ‘always-on’ pupillary light reflex (PLR)-aware pupil size measurement in eyewear, including improving the reliability of pupil-based cognitive and affective load monitoring and enabling PLR-based diagnosis of cognitive and eye-related diseases which have neurological symptoms manifested in the form of aberrant PLR responses. However the detection of PLR responses for application in eyewear devices for everyday usage, beyond PLR measurement in confined clinical sessions, has not been investigated. To this end a means of characterising PLRs in less controlled environmental settings is investigated and subsequently a method of PLR detection is developed and evaluated. A low-cost head-mounted web camera was used to record near-field eye video sequences which were processed with the Self-Tuning Threshold Algorithm for pupil diameter estimation and blink detection. PLR was induced by luminance change of a monitor and brightness change of the displayed image on a monitor. A transient model-based PLR detection algorithm which utilizes the general correlation of PLR amplitude and velocity was developed and evaluated on the datasets in terms of false alarm and false rejection rates. The findings from this research suggest that the PLR can be detected reliably using low-cost wearable pupil-measurement systems without using a separate sensor for detecting the luminance conditions. The correlation between pupil diameter amplitude and maximum velocity of PLR was shown to be sufficiently consistent for PLR detection.