Abstract
Emotions may not only be perceived by humans, but could also be identified and recognized by a machine. Emotion recognition through pattern analysis can be used in expert systems, lie detectors, medical emergencies, as well as during rescue operations to quickly identify people in distress. This paper describes a system capable of recognizing emotions based on the arm movement. Features extracted from 3D skeleton using Kinect sensor are classified by five commonly used machine learning techniques: K nearest neighbors, SVM, Decision tree, Neural Network and Naive Bayes. A genetic algorithm is then invoked to find the best system parameters to achieve the higher recognition rate. The system achieved 98.96% average accuracy on the experimental dataset.
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Acknowledgements
Authors would like to acknowledge NSERC, MITACS funding and Switzerland international exchange program. We also grateful to all members of the Biometric Technologies Laboratory, Department of Computer Science, University of Calgary.
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Maret, Y., Oberson, D., Gavrilova, M. (2018). Identifying an Emotional State from Body Movements Using Genetic-Based Algorithms. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_44
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