Volume 17, Issue 4 (Winter 2016)
Advances in Cognitive Sciences 2016, 17(4): 45-62 |
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1- PhD Student, Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran,Iran.
2- Professor, Medical Physics Department, Faculty of Medical Sciences, Tarbiat Modares University, Tehran,Iran.
3- Electrical Engineering Department, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran,Iran.
2- Professor, Medical Physics Department, Faculty of Medical Sciences, Tarbiat Modares University, Tehran,Iran.
3- Electrical Engineering Department, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran,Iran.
Abstract: (2839 Views)
Introduction: In this study, we propose a new adaptive method for fusing multiple emotional modalities to improve the performance of an emotion recognition system.
Method: Three-channel forehead biosignals, along with peripheral physiological measurements (blood volume pressure, skin conductance, and interbeat intervals), were utilized as emotional modalities. Six basic emotions, i.e., anger, sadness, fear, disgust, happiness, and surprise were elicited by displaying preselected video clips for each of the 25 participants in the experiment. In the proposed emotion recognition system, recorded signals with the formation of three classification units identified the emotions independently. The results were then fused using the adaptive weighted linear model to produce the final result. Each classification unit is assigned a weight that minimizes the squared error of the ensemble system.
Results: The results showed that, the proposed fusion method outperformed all individual classifiers and emotion systems that were designed based on feature level fusion and classifiers fusion using the majority voting method. Using the support vector machine (SVM) classifier, an overall recognition accuracy of 88% was obtained in identifying the intended emotional states. Also, applying only the forehead or the physiological signals in the proposed fusion scheme indicates that designing a reliable emotion recognition system is feasible without the need for additional emotional modalities.
Conclusion: The results suggest using adaptive fusion of classification units in the design of multimodal emotions recognition system.
Method: Three-channel forehead biosignals, along with peripheral physiological measurements (blood volume pressure, skin conductance, and interbeat intervals), were utilized as emotional modalities. Six basic emotions, i.e., anger, sadness, fear, disgust, happiness, and surprise were elicited by displaying preselected video clips for each of the 25 participants in the experiment. In the proposed emotion recognition system, recorded signals with the formation of three classification units identified the emotions independently. The results were then fused using the adaptive weighted linear model to produce the final result. Each classification unit is assigned a weight that minimizes the squared error of the ensemble system.
Results: The results showed that, the proposed fusion method outperformed all individual classifiers and emotion systems that were designed based on feature level fusion and classifiers fusion using the majority voting method. Using the support vector machine (SVM) classifier, an overall recognition accuracy of 88% was obtained in identifying the intended emotional states. Also, applying only the forehead or the physiological signals in the proposed fusion scheme indicates that designing a reliable emotion recognition system is feasible without the need for additional emotional modalities.
Conclusion: The results suggest using adaptive fusion of classification units in the design of multimodal emotions recognition system.
Keywords: Emotion Recognition System, Pattern Recognition, Forehead Bioelectric Signals, Physiological Signals, Decision Level Fusion, Adaptive Fusion
Type of Study: Research |
Subject:
Special
Received: 2015/09/21 | Accepted: 2015/11/21 | Published: 2015/12/22
Received: 2015/09/21 | Accepted: 2015/11/21 | Published: 2015/12/22
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