Reckoning of Emotions Through Recognition of Posture Features

Abstract

Emotions are noteworthy signs to understand the intentions of others peoples during communication with them. Similarly, in order to identify the different emotional states of individual such as joy, sadness, and anger, using facial expressions and vocal tone are less effective due to the variations in facial and vocal outputs. Thus, for recognizing emotions intensely without variation physique postures analysis can give effective outputs. Thus, physique posture analysis of the individual can be obtained by mapping the joints using Reeb graph which plots all the joints into carvative structure to learn posture, and the angles between the joints of posture are detected by law of cosines which depicts the vigorous expression along with postures. In addition to that much more reliable recognition of emotions without distractions can be obtained through the detailed features by preprocessing the input image through the fusion of Median and Wiener filter which avoids all the five types of noise occurrence so detailed features such as Invariant, Depth sequential silhouettes and Spatiotemporal body joint can be obtained to aid efficient analysis of posture that can pave a way to identify the different emotions by tactic Tree based classifier to get better performance in terms of execution time and accuracy.