To Recognize Human Emotions Based on Facial Expression Recognition : A Literature Survey

Authors(2) :-Komal D. Khawale, D. R. Dhotre

Facial Emotion Recognition has been a very significant issue and an advanced area of research in the field of Human- Machine Interaction and Image Processing. Human-Machine relation is a major field for that different approaches have been proposed for developing methods for recognition of automated facial emotion analysis using not only facial expressions, also speech recognition. Facial expression detection the multiple varieties of human faces like texture, color, shape, expressions etc. are considered. Firstly, to detect a facial emotions of the human with variations in the facial movements including mouth, eyes, and nose are to be determined and after that considering those features using a very good classifier to recognize the human emotions. This paper gives a brief summary of emotion recognition methods like Feature Fusion, Deep Auto-Encoder, Sigma Pi-Neural Network, Genetic Algorithm, PHOG and Hierarchical Expression Model etc. which are used to recognize human emotions are presented.

Authors and Affiliations

Komal D. Khawale
Department of Computer Engineering, Amravati University, SSGMCE, Shegaon, Maharashtra, India
D. R. Dhotre
Department of Computer Engineering, Amravati University, SSGMCE, Shegaon, Maharashtra, India

Human Emotion Recognition, PHOG, Emotion Recognition, Expression Recognition.

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Publication Details

Published in : Volume 2 | Issue 1 | January-February 2017
Date of Publication : 2017-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 35-41
Manuscript Number : CSEIT172110
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Komal D. Khawale, D. R. Dhotre, "To Recognize Human Emotions Based on Facial Expression Recognition : A Literature Survey", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 1, pp.35-41, January-February-2017.
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