Volume 3 Number 7 (Jul. 2008)
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JCP 2008 Vol.3(7): 39-47 ISSN: 1796-203X
doi: 10.4304/jcp.3.7.39-47

A New Information Fusion Method for Bimodal Robotic Emotion Recognition

Meng-Ju Han1, Jing-Huai Hsu1, Kai-Tai Song1, Fuh-Yu Chang2
1Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
2Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan R.O.C.

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

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Cite: Meng-Ju Han, Jing-Huai Hsu, Kai-Tai Song, Fuh-Yu Chang, "A New Information Fusion Method for Bimodal Robotic Emotion Recognition," Journal of Computers vol. 3, no. 7, pp.39-47, 2008.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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