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Abstract
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Article Information:
Object Analysis of Human Emotions by Contourlets and GLCM Features
R. Suresh and S. Audithan
Corresponding Author: R. Suresh and S. Audithan
Submitted: June 08, 2014
Accepted: July 19, 2014
Published: August 20, 2014 |
Abstract:
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Facial expression is one of the most significant ways to express the intention, emotion and other nonverbal messages of human beings. A computerized human emotion recognition system based on Contourlet transformation is proposed. In order to analyze the presented study, seven kind of human emotions such as anger, fear, happiness, surprise, sadness, disgust and neutral of facial images are taken into account. The considered emotional images of human are represented by Contourlet transformation that decomposes the images into directional sub-bands at multiple levels. The features are extracted from the obtained sub-bands and stored for further analysis. Also, texture features from Gray Level Co-occurrence Matrix (GLCM) are extracted and fused together with contourlet features to obtain higher recognition accuracy. To recognize the facial expressions, K Nearest Neighbor (KNN) classifier is used to recognize the input facial image into one of the seven analyzed expressions and over 90% accuracy is achieved.
Key words: Contourlet transform, emotion recognition, facial expression, nearest neighbor classifier, , ,
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Cite this Reference:
R. Suresh and S. Audithan, . Object Analysis of Human Emotions by Contourlets and GLCM Features. Research Journal of Applied Sciences, Engineering and Technology, (7): 856-862.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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