DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-5,Issue-3 ( Mar, 2018 ) | |||||||||
Paper Title |
Emotion Tracking and Grading based on Sophisticated Statistical Approach | |||||||||
Author Name |
Mohammad Alamgir Hossain, Hamad Zogan, G Sanyal | |||||||||
Affilition |
Department of Computer Science, JAZAN UNIVERSITY, KSA NIT, Durgapur, West Bengal, India | |||||||||
Pages |
9-13 | |||||||||
Abstract |
Identification andanalysis of human emotion from facial expression is an important and demanding aspect in present research domain. The emotions detection and their classification is a red hot topic in the present era because almost all types of visual surveillance work are depended on this issue. The job of segregating a people or group of people depending on his or her emotion is very difficult. During the previousthree-four decades the scholars and experts with their remarkablestruggleprojecteddiversesystematic method that help to rank and correlate human emotion expressions along with human feelings. In this paper, it is proposed to incorporatesophisticated and novel approach based on statistical and mathematical functions and formulasfor tracking emotion from human image and categorizing the expressed moment of recognized mood that has been marked out and accepted. Firstly, human-face-emotion-infois taken to collect the maximum rank from expressed-emotion. With the collected expressed Emotionthe parameters is set based on values X, Y and Z coordinates to store them in the Emotion-Info-Mask. Secondly, the values are sorted and stored into the database. Lastly, from the coordinate values it is easy to identify the specified emotion and subsequently mood of the human being. Using the proposed algorithm we have achieved better result as compared to other researchers. Our emotion detection algorithm has proved its superiority with the result of 99.01% in Fear-Emotion detection, 91.35% in Normal emotion detection and 87.41% in smiley emotion detection. Keywords: Emotion, Recognition, Emotion-Info-Mask, mathematical-detection, histogram, Emotion-Face-Mask | |||||||||
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