Matthias Wimmer, Björn Schuller, Dejan Arsic, Bernd Radig, and Gerhard Rigoll. Low-level Fusion of Audio and Video Feature for Multi-modal Emotion Recognition. In 3rd International Conference on Computer Vision Theory and Applications (VISAPP), pages 145–151, Madeira, Portugal, January 2008.

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Abstract

Bimodal emotion recognition through audiovisual feature fusion has been shown superior over each individual modality in the past. Still, synchronization of the two streams is a challenge, as many vision approaches work on a frame basis opposing audio turn- or chunk-basis. Therefore, late fusion schemes such as simple logic or voting strategies are commonly used for the overall estimation of underlying affect. However, early fusion is known to be more effective in many other multimodal recognition tasks. We therefore suggest a combined analysis by descriptive statistics of audio and video Low-Level-Descriptors for subsequent static SVM Classification. This strategy also allows for a combined feature-space optimization which will be discussed herein. The high effectiveness of this approach is shown on a database of 11.5h containing six emotional situations in an airplane scenario.

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