TY  - JOUR
AU  - Ahammed, Kawser 
PY  - 2015
TI  - Identification of Human Emotions via Univariate and Multivarite Multiscale Entropy
JF  - American Journal of Engineering and Applied Sciences
VL  - 8
IS  - 3
DO  - 10.3844/ajeassp.2015.410.416
UR  - https://thescipub.com/abstract/ajeassp.2015.410.416
AB  - 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.