JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 7    Date : July 2008

A New Information Fusion Method for Bimodal Robotic Emotion Recognition
Meng-Ju Han, Jing-Huai Hsu, Kai-Tai Song, and Fuh-Yu Chang
Page(s): 39-47
Full Text:
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Abstract
Emotion recognition has become a popular area in human-robot interaction research. Through
recognizing facial expressions, a robot can interact with a person in a more friendly manner. In this
paper, we proposed a bimodal emotion recognition system by combining image and speech
signals. A novel probabilistic strategy has been studied for a support vector machine (SVM)-based
classification design to assign statistically information-fusion weights for two feature modalities.
The fusion weights are determined by the distance between test data and the classification
hyperplane and the standard deviation of training samples. In the latter bimodal SVM classification,
the recognition result with higher weight is selected. The complete procedure has been
implemented in a DSP-based embedded system to recognize five facial expressions on-line in real
time. The experimental results show that an average recognition rate of 86.9% is achieved, a 5%
improvement compared to using only image information.

Index Terms
emotion recognition, probabilistic SVM, DSP-based image and audio processing system,
humanrobot interaction.