Abstract
The analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, neuroscience, 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.
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Hadjerci, O., Lablack, A., Bilasco, I.M., Djeraba, C. (2014). Affect Recognition Using Magnitude Models of Motion. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_33
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DOI: https://doi.org/10.1007/978-3-319-04117-9_33
Publisher Name: Springer, Cham
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