Energy-Aware Benchmarking of Wearable Eye Trackers

The number of devices embedding eye tracking (ET) capabilities, such as portable webcam-based consumer devices and wearable ones, such as headsets and smart eyeglasses, is rapidly increasing, making this technology truly pervasive. Despite the large number of papers and reviews discussing data quality and benchmarking of trackers, none of them is addressing the trade-off between power consumption, speed and accuracy. Power dissipation is typically dominated by signal processing to extract gaze information from sensors embedded in the glasses. This compromise is crucial for smart glasses, powered by miniature batteries, offering a typical power budget of a few tens of mW for ET. Here we propose a simple benchmarking flow for wearable trackers, focused on power consumption, as well as accuracy, precision and sampling rate, and based on three complementary test setups. We report the preliminary results of the experimental characterization of 6 commercial trackers in the first static setup and we show a comparison of their performance based on a single figure of merit.