An Optimize Feature Extraction Approach for Facial Emotion Detection System

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Volume 3, Issue 7 (July, 2017)
Publication No:
Ashveen Kaur, Er. Sukhpreet Kaur
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Recently there has been a growing interest in improving the interaction between humans and computers. It is argued that to achieve effective human-computer intelligent interaction, there is a need for the computer to interact naturally with the user, similar to the way humans interact. Humans interact with each other mostly through speech, but also through body gestures to emphasize a certain part of speech and/or display of emotions. Emotions are displayed by visual, vocal and other physiological means. There is more and more evidence appearing that shows that emotional skills are part of what is called ‘intelligence’. One of the most important ways for humans to display emotions is through facial expressions. So, this study, will propose a computational model of emotions recognition based on face biometrics, which is fast, reasonably simple, and accurate in constrained environments using hybridization of SIFT and PSO algorithm, in which features extraction is done by SIFT, feature reduction is done by PSO and classification is done by hamming distance. In proposed work six emotions will be recognized such as sad, happy, neutral, disgusting, aggressive and surprised.

Emotion Recognition, Feature extraction, feature reduction, classification.