The Importance Distribution of Drivers’ Facial Expressions Varies over Time!

Abstract

Facial Expressions are valuable data sources for advanced HumanVehicle Interaction designs. However, existing works always consider the whole facial expressions as input, which restricts the design space for detailed optimizations. In this work, we make the hypothesis that facial expressions can exhibit significant variations during the driving procedure. Our goal in this work-in-progress is to justify this hypothesis, by performing detailed characterizations on the drivers’ facial expressions. To this end, we leverage Local Binary Fitting, a novel mechanism for selecting representative feature points from facial images on the fly, for our characterizations. Our characterizations reveal that, among six major components of facial feature points, there are significant variations of correlations with a certain vehicle status (i.e. Vehicle Speed), in terms of (1) the time spots during the driving procedure; and (2) the gender of the drivers. We believe our works can serve as a starting point to incorporate the characteristics of our findings with a great amount of adaptive and personalized Human-Vehicle Interaction designs.

Publication
AutomotiveUI ‘21 Adjunct – 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI'21)
Zeyu Xiong
Zeyu Xiong
Incoming PhD Student

My research interests include Human-Computer Interaction, Human-AI Collaboration, Accessibility, Ubiquitous Computing and Computer Music.