Face2Statistics: User-Friendly, Low-Cost and Effective Alternative to In-vehicle Sensors/Monitors for Drivers

Abstract

We present Face2Statistics, a comprehensive roadmap to deliver user-friendly, low-cost and effective alternatives for extracting drivers’ statistics. Face2Statistics is motivated by the growing importance of multi-modal statistics for Human-Vehicle Interaction, but existing approaches are user-unfriendly, impractical and cost-ineffective. To this end, we leverage Face2Statistics to build a series of Deep-Neural-Network-driven predictors of multi-modal statistics, by taking facial expressions as input only. We address two outstanding issues of the current design, and then (1) leverage HSV color space; and (2) Conditional Random Field to improve the robustness of Face2Statistics in terms of prediction accuracy and degree of customization. Our evaluations show that, Face2Statistics can be effective alternatives to sensors/monitors for Heart Rate, Skin Conductivity and Vehicle Speed. We also perform the breakdown analysis to justify the effectiveness of our optimizations. Both source codes and trained models of Face2Statistics are online at https://github.com/unnc-ucc/Face2Statistics.