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A Novel Neuro Fuzzy Approach to Human Emotion Determination

Chatterjee, Suvam and Shi, Hao (2010) A Novel Neuro Fuzzy Approach to Human Emotion Determination. In: 2010 digital Image computing: techniques and applications : DICTA 2010 : proceedings. Zhang, Jian, Shen, Chunhua, Geers, Glenn and Wu, Qiang, eds. IEEE, Piscataway, N.J., pp. 282-287.

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Facial expression is the most natural and instinctive means for human beings to communicate with each other Automatic analysis of human facial expression remains a very challenging area of research in computer vision and machine learning. In this paper, a novel human emotion detection is proposed based on well-known local binary pattern (LBP) and a newly developed feature matrix. Facial feature is extracted using LBP. A unique feature matrix is then combined to apply to Adaptive Neuro Fuzzy Inference System to generate five facial expression models, namely, happy, sad, angry, disgust and surprise. A number of experiments are carried out on facial expression determination with different LBP techniques by varying the number of points and radius of neighbourhood to JAFFE face database. The proposed system achieves very high accuracy with LBP(16,2) which has outperformed most of the existing methods.

Item Type: Book Section
ISBN: 978-1-4244-8816-2 (print), 9780769542713 (online)
Additional Information:

Conference held: 1-3 December 2010, Sydney, Australia

Uncontrolled Keywords: ResPubID19683, faces, facial features, feature extraction, histograms, pixels, LBP, adaptive neuro fuzzy inference, facial feature extraction, feature matrix, human emotion determination, human facial expression, local binary patterns, facial recognition, human emotion determination, Japanese Female Facial Expression Database, JAFFE
Subjects: Current > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > SEO Classification > 8902 Computer Software and Services
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 28 Mar 2013 05:38
Last Modified: 02 Oct 2020 00:45
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Citations in Scopus: 13 - View on Scopus

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