Implicit Media Tagging And Affect Prediction From Rgb-D Video Of Spontaneous Facial Expressions

We present a method that automatically evaluates emotional response from spontaneous facial activity. The automatic evaluation of emotional response, or affect, is a fascinating challenge with many applications. Our approach is based on the inferred activity of facial muscles over time, as automatically obtained from an RGB-D video recording of spontaneous facial activity. Our contribution is two-fold: First, we constructed a database of publicly available short video clips, which elicit a strong emotional response in a consistent manner across different individuals. Each video was tagged by its characteristic emotional response along 4 scales: Valence, Arousal, Likability and Rewatch (the desire to watch again). The second contribution is a two-step prediction method, based on learning, which was trained and tested using this database of tagged video clips. Our method was able to successfully predict the aforementioned 4 dimensional representation of affect, achieving high correlation (0.87-0.95) between the predicted scores and the affect tags. As part of the prediction algorithm we identified the period of strongest emotional response in the viewing recordings, in a method that was blind to the video clip being watched, showing high agreement between independent viewers. Finally, inspection of the relative contribution of different feature types to the prediction process revealed that temporal facets contributed more to the prediction of individual affect than to media tags.