Affect Recognition Using Magnitude Models of Motion
Résumé
The analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, neu-roscience, and related disciplines. We focus on the recognition of the affect state of a single person from video streams. We create a model that allows to estimate the state of four affective dimensions of a person which are arousal, anticipation, power and valence. This sequence model is composed of a magnitude model of motion constructed from a set of point of interest tracked using optical flow. The state of the affective dimension is then predicted using SVM. The experimentation has been performed on a standard dataset and has showed promising results.