Emotion Recognition Based on General-ized Gamma Distribution

Abstract

It is highly difficult to identify the emotions of a person. Literature accessible to recognize the emotions in case of immobilized personnel is limited to the outcome obtainable from the machines only. In this process, brain computer communication is utilized using neuro-scan machines like Encephalography (EEG), to recognize the feeling of immobilized persons. It uses the physiological signals accessible from EEG data extracted from the brain signals of immobilized personnel and tries to find out the emotions, but these results vary from machine to machine, and there exists no consistency by which one can identify the thoughts of the brain diseased personnel precisely. In this manuscript a novel technique is projected by using Generalized Gamma Mixture models (GGMM). The advantage of considering GGMM is its ability of extracting the emotions closely even in a noisy environment. The outcomes of the proposed method exceed the accuracy rates of conventional systems.