Research Article Open Access

Identification of Human Emotions via Univariate and Multivarite Multiscale Entropy

Kawser Ahammed1
  • 1 University of Dhaka, Bangladesh
American Journal of Engineering and Applied Sciences
Volume 8 No. 3, 2015, 410-416

DOI: https://doi.org/10.3844/ajeassp.2015.410.416

Submitted On: 5 June 2015 Published On: 23 July 2015

How to Cite: Ahammed, K. (2015). Identification of Human Emotions via Univariate and Multivarite Multiscale Entropy. American Journal of Engineering and Applied Sciences, 8(3), 410-416. https://doi.org/10.3844/ajeassp.2015.410.416

Abstract

This work analyzes the emotions of human in terms of complexity. This analysis is achieved by applying both univariate and multivariate multiscale entropy methods on a multimodal dataset. Most of the contemporary human-computer interaction systems are unable to identify human affective states. So, the benefit of analyzing human emotions is to fill this gap by detecting human affective states. The univariate and multivariate multiscale entropy analysis curves obtained using multimodal dataset show differences in terms of complexity among different affective states, which can be used for emotion detection and classification for machine vision applications.

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Keywords

  • Multimodal Dataset
  • Affective States Computing
  • Emotion Classification
  • Machine Vision Applications