Image fusion and enhancement based on energy of the pixel using Deep Convolutional Neural Network.

This paper presents a novel and generic framework for the recognition of emotions using human body expression like head, hand and leg movements. Whole body movements are among the main visual stimulus categories that are naturally associated with faces and the neuro scientific investigation of how body expressions are processed has entered the research agenda this last decade. The database was composed of 254 whole body expressions from 46 actors expressing four emotions (anger, fear, happiness, and sadness). In all pictures the face of the actor was blurred and participants were asked to categorize the emotions expressed in the stimuli in a four alternative-forced-choice task. Using Deep Convolutional Neural Network (DCNN), the input images are trained and modeled. Then the model can be tested by test images for recognizing human emotion from non-verbal communication. [ABSTRACT FROM AUTHOR]