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Emotion Recognition Approach Using Multilayer Perceptron Network and Motion Estimation

Emotion Recognition Approach Using Multilayer Perceptron Network and Motion Estimation

Mohamed Berkane, Kenza Belhouchette, Hacene Belhadef
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 16
ISSN: 1947-9093|EISSN: 1947-9107|EISBN13: 9781522566205|DOI: 10.4018/IJSE.2019010102
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MLA

Berkane, Mohamed, et al. "Emotion Recognition Approach Using Multilayer Perceptron Network and Motion Estimation." IJSE vol.10, no.1 2019: pp.38-53. http://doi.org/10.4018/IJSE.2019010102

APA

Berkane, M., Belhouchette, K., & Belhadef, H. (2019). Emotion Recognition Approach Using Multilayer Perceptron Network and Motion Estimation. International Journal of Synthetic Emotions (IJSE), 10(1), 38-53. http://doi.org/10.4018/IJSE.2019010102

Chicago

Berkane, Mohamed, Kenza Belhouchette, and Hacene Belhadef. "Emotion Recognition Approach Using Multilayer Perceptron Network and Motion Estimation," International Journal of Synthetic Emotions (IJSE) 10, no.1: 38-53. http://doi.org/10.4018/IJSE.2019010102

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Abstract

Man-machine interaction is an interdisciplinary field of research that provides natural and multimodal ways of interaction between humans and computers. For this purpose, the computer must understand the emotional state of the person with whom it interacts. This article proposes a novel method for detecting and classify the basic emotions like sadness, joy, anger, fear, disgust, surprise, and interest that was introduced in previous works. As with all emotion recognition systems, the approach follows the basic steps, such as: facial detection and facial feature extraction. In these steps, the contribution is expressed by using strategic face points and interprets motions as action units extracted by the FACS system. The second contribution is at the level of the classification step, where two classifiers were used: Kohonen self-organizing maps (KSOM) and multilayer perceptron (MLP) in order to obtain the best results. The obtained results show that the recognition rate of basic emotions has improved, and the running time was minimized by reducing resource use.

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