  Wearable pupil detection systems often separate the analysis of the captured
wearer's eye images for wirelessly-tethered back-end systems. We argue in this
paper that investigating hardware-software co-designs would bring along
opportunities to make such systems smaller and more efficient. We introduce an
open-source embedded system for wearable, non-invasive pupil detection in
real-time, on the wearable, embedded platform itself. Our system consists of a
head-mounted eye tracker prototype, which combines two miniature camera systems
with Raspberry Pi-based embedded system. Apart from the hardware design, we
also contribute a pupil detection pipeline that operates using edge analysis,
natively on the embedded system at 30fps and run-time of 54ms at 480x640 and
23ms at 240x320. Average cumulative error of 5.3368px is found on the LPW
dataset for a detection rate of 51.9\% with our detection pipeline. For
evaluation on our hardware-specific camera frames, we also contribute a dataset
of 35000 images, from 20 participants.

Computer Vision and Pattern Recognition;Human-Computer Interaction