A real-time, multimodal, and dimensional affect recognition system

College

College of Computer Studies

Department/Unit

Computer Technology

Document Type

Conference Proceeding

Source Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

7458 LNAI

First Page

241

Last Page

249

Publication Date

10-25-2012

Abstract

This study focuses on the development of a real-time automatic affect recognition system. It adapts a multimodal approach, where affect information taken from two modalities are combined to arrive at an emotion label that is represented in a valence-arousal space. The SEMAINE Database was used to build the affect model. Prosodic and spectral features were used to predict affect from the voice. Temporal templates called Motion History Images (MHI) were used to predict affect from the face. Prediction results from the face and voice models were combined using decision-level fusion. Using support vector machine for regression (SVR), the system was able to correctly identify affect label with a root mean square error (RMSE) of 0.2899 for arousal, and 0.2889 for valence. © 2012 Springer-Verlag.

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Digitial Object Identifier (DOI)

10.1007/978-3-642-32695-0_23

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