Facial Micro-Expression Detection under Variable Road Condition for Evaluation of Driver’s Performance

Abstract

The emotional state of the driver has a direct impact on driving. Hence, to ensure road safety, it is important to monitor driver’s emotional state. This challenging task can be achieved through analyzing micro-expressions as they are linked with a person’s emotional state and emerge on face even under situations where a person is trying to conceal true emotions. Moreover, it’s easy to acquire facial data while driving than any other stress signal. This research focuses on identifying micro-expressions linked with stressful emotional state in drivers. The physiological parameters like heart rate and stress value based on heart rate variability are also monitored as they fluctuate easily under emotional changes within the body. This research considered the emotions of happiness, sadness, surprise, anger, fear and disgust. To evaluate stress within drivers, the dominant emotion behind detected micro-expression is found through an emotion detection open source code. The results show a high F1 score for the identified micro-expressions i.e. 1.00, 0.947, 0.933 and 0.85. These findings can help in face readings where stress detection is required and can contribute towards better systems in cars to ensure road safety and manage stress.