AU - Khezri, Mahdi AU - Firoozabadi, Mohammad AU - Sharafat Professor, Ahmad Reza TI - Adaptive Fusion of Forehead and Physiological Signals upon Emotion Recognition PT - JOURNAL ARTICLE TA - icss JN - icss VO - 17 VI - 4 IP - 4 4099 - http://icssjournal.ir/article-1-357-en.html 4100 - http://icssjournal.ir/article-1-357-en.pdf SO - icss 4 ABĀ  - 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. CP - IRAN IN - LG - eng PB - icss PG - 45 PT - Research YR - 2016