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Reseach Article

Human Expression Recognition using Facial Features

Published on April 2014 by G. Saranya, G. Mary Amirtha Sagayee
International Conference on Knowledge Collaboration in Engineering
Foundation of Computer Science USA
ICKCE - Number 1
April 2014
Authors: G. Saranya, G. Mary Amirtha Sagayee

G. Saranya, G. Mary Amirtha Sagayee . Human Expression Recognition using Facial Features. International Conference on Knowledge Collaboration in Engineering. ICKCE, 1 (April 2014), 6-9.

author = { G. Saranya, G. Mary Amirtha Sagayee },
title = { Human Expression Recognition using Facial Features },
journal = { International Conference on Knowledge Collaboration in Engineering },
issue_date = { April 2014 },
volume = { ICKCE },
number = { 1 },
month = { April },
year = { 2014 },
issn = 0975-8887,
pages = { 6-9 },
numpages = 4,
url = { /proceedings/ickce/number1/16139-1003/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 International Conference on Knowledge Collaboration in Engineering
%A G. Saranya
%A G. Mary Amirtha Sagayee
%T Human Expression Recognition using Facial Features
%J International Conference on Knowledge Collaboration in Engineering
%@ 0975-8887
%N 1
%P 6-9
%D 2014
%I International Journal of Computer Applications

Facial expression recognition can be used in many applications such as surveillances, security, gaming, customer care center and human computer interactions during non verbal communication. The user's emotion state makes the system to recognize the emotions and to interact with the human effectively. Facial expressions can notify the changes in the user's emotional state and these can be detected and interpreted by a computer. This feature extraction program captures each facial frame and extracts all those feature points, which are sent to the classifier. When emotional changes detected, the output of the emotion classifier constitutes an emotion code. These emotional face expressions and voice tones are combined with the verbal behavior. Support vector machine (SVM) is used to classify the expressions. Then the output can be displayed as a person's various emotional expressions such as fear, happy, sad, disgust, anger, fear and shame in a self assessment test. In the proposed work the expression recognition is done effectively for several expressions. The expression can be recognized from video by converting the video file to the frame format. The recognition can be done by the cost effective manner by using web camera to capture the human emotions.

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Index Terms

Computer Science
Information Sciences


Recognition Svm Score Values Emotional State Pca