Analysis of facial features of drivers under cognitive and visual distractions

Drivers are exposed to a growing risk of being distracted with the recent development of in-vehicle systems for navigation, communication and infotainment. As a result, there is a need for tracking systems that can monitor the drivers´ attention. This study investigates driver distractions using a multimodal corpus collected from real world driving scenarios. The paper focuses on facial cues automatically extracted from a frontal camera facing the driver. We conducted subjective evaluations by external observers to assess the perceived visual and cognitive distraction of drivers performing secondary tasks. The data is divided into two classes - distracted and normal. This partition is separately created for visual and cognitive scores. Binary classifiers are built with features describing action units (AU) and gaze (e.g., head poses). The classifiers achieve 80.8% F-score for visual distractions, and 73.8% F-score for cognitive distractions. The study identifies features that are relevant for detecting both types of distractions. Furthermore, the paper presents a logistic regression analysis to identify facial features that are useful for detecting samples in which cognitive distraction scores are not related to visual distraction scores. The analysis reveals the benefits of using AU in cognitive related distraction detection.