Affective Meeting Video Analysis

In this paper we examine the affective content of meeting videos. First we asked five subjects to manually label three meeting videos using continuous response measurement (continuous-scale labeling in real-time) for energy and valence (the two dimensions of the human affect space). Then we automatically extracted audio-visual features to characterize the affective content of the videos. We compare the results of manual labeling and low-level automatic audio-visual feature extraction. Our analysis yields promising results, which suggest that affective meeting video analysis can lead to very interesting observations useful for automatic indexing. © 2005 IEEE.