Low-level Fusion of Audio and Video Feature for Multi-modal Emotion Recognition
Year: 2008
Type: conference-paper
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. 1
Authors: Matthias Wimmer, Björn Schuller, Dejan Arsic, Gerhard Rigoll, Bernd Radig
Institution: Technical University of Munich
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W1869734671