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Open AccessArticle

Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

Electrical Engineering and Computer Science Department, The University of Toledo, 2801 W Bancroft St, MS 308, Toledo, OH 43606, USA
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Sensors 2019, 19(8), 1897; https://doi.org/10.3390/s19081897
Received: 24 March 2019 / Revised: 18 April 2019 / Accepted: 18 April 2019 / Published: 21 April 2019
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Abstract

Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human–machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition. View Full-Text
Keywords: automatic facial emotion recognition; intensity of emotion recognition; behavioral biometrical systems; machine learning automatic facial emotion recognition; intensity of emotion recognition; behavioral biometrical systems; machine learning
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Mehta, D.; Siddiqui, M.F.H.; Javaid, A.Y. Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study. Sensors 2019, 19, 1897.

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