Face analysis of aggressive moods in automobile driving using mutual subspace method

Aggressive affections of automobile drivers such as irritation often cause unpleasant experiences and ultimately road rage. Detecting their cues from drivers´ behaviors and obviating undesirable consequences is the most important role of automobile navigation for future safe driving. Facial expressions have been found to be a useful indicator of the driver´s affection due to the robustness in monitoring drivers compared with other sensors. Affection consists of two kinds of factors: emotion (impulsive and strong) and mood (long lasting and subtle), where mood biases what kind of emotions to come up. Although moods dominate emotions, conventional approach in facial expression analysis has focused on emotion rather than mood in this context. The technical difficulty in analyzing moods is that there is no neutral expression that has been used as the firm reference for classifying facial expressions because the neutral is the mood itself and varies over time. The proposed method parameterizes appearance changes of face image sequence using mutual subspace method, and estimates the levels of aggressive mood, i.e., irritation and tense. Experimental results that used simulated facial expressions gave the optimal configuration of the proposed method.