Factorizing speaker, lexical and emotional variabilities observed in facial expressions

An effective human computer interaction system should be equipped with mechanisms to recognize and respond to the affective state of the user. However, spoken message conveys different communicative aspects such as the verbal content, emotional state and idiosyncrasy of the speaker. Each of these aspects introduces variability that will affect the performance of an emotion recognition system. If the models used to capture the expressive behaviors are constrained by the lexical content and speaker identity, it is expected that the observed uncertainty in the channel will decrease, improving the accuracy of the system. Motivated by these observations, this study aims to quantify and localize the speaker, lexical and emotional variabilities observed in the face during human interaction. A metric inspired in mutual information theory is proposed to quantify the dependency of facial features on these factors. This metric uses the trace of the covariance matrix of facial motion trajectories to measure the uncertainty. The experimental results confirm the strong influence of the lexical information in the lower part of the face. For this facial region, the results demonstrate the benefit of constraining the emotional model on the lexical content. The ultimate goal of this research is to utilize this information to constrain the emotional models on the underlying lexical units to improve the accuracy of emotion recognition systems.