Ahn, S.J., Bailenson, J.N., Fox. J, & Jabon, M.E. (2010). Using Automated Facial Expression Analysis for Emotion and Behavior Prediction. in Doeveling, K., von Scheve, C., & Konjin, E. A. (Eds.), Handbook of Emotions and Mass Media (349-369). London/New York: Routledge.
In this chapter, we propose a model of approaching facial expression detection and analysis that goes beyond category-based measurements by incorporating automated technologies that make create and automatically improve prediction models based on raw facial feature movements. Using a computer equipped with a small camera, tracking software, and machine learning (an automated form of computer generated self-enhancement), we are able to select the most relevant facial features out of the massive collection of raw data and improve the prediction model in a time- and cost-efficient way. This allows us to utilize the raw data without fitting them into predefined categories, giving us greater analytical power than conventional category-based predictions. Moreover, our suggested methodology is notably unobtrusive when compared to other behavioral measures such as physiological measures, which usually require numerous sensors to be attached to the body.