Optimization of Event Camera Bias Settings for a Neuromorphic Driver Monitoring System

Event cameras provide a novel imaging technology for high-speed analysis of localized facial motions such as eye gaze, eye-blink and micro-expressions by taking input at the level of an individual pixel. Due to this capability, and lightweight amount of the output data these cameras are being evaluated as a viable option for driver monitoring systems (DMS). This research is the first to investigate the impact of bias modifications on the event-based DMS output and propose an approach for evaluating and comparing DMS performance. The study investigates the impact of pixel-bias alteration on DMS features, which are: face tracking, blink counting, head pose and gaze estimation. In order to do this, new metrics are proposed to evaluate how effectively the DMS performs for each feature and overall. These metrics identify stability as the most important factor for face tracking, head pose estimations, and gaze estimations. The accuracy of the blink counting, which is the key component of this function, is also evaluated. Finally, all of these metrics are used to assess the system’s overall performance. The effects of bias changes on each feature are explored on a number of human subjects with their consent. The newly proposed metrics are used to determine the ideal bias ranges for each DMS feature and the overall performance. The results indicate that the DMS’s functioning is enhanced with proper bias tuning based on the proposed metrics.