Emotion Recognition from the Lip-Contour of a Subject Using Artificial Bee Colony Optimization Algorithm

This paper provides an alternative approach to emotion recognition from the outer lip-contour of the subjects. Subjects exhibit their emotions through their facial expressions, and the lip region is segmented from their facial images. A lip-contour model has been developed to represent the boundary...

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Published inSwarm, Evolutionary, and Memetic Computing Vol. 7076; pp. 610 - 617
Main Authors Halder, Anisha, Rakshit, Pratyusha, Chakraborty, Aruna, Konar, Amit, Janarthanan, Ramadoss
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2011
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Algorithms & data structures
artificial bee colony optimization algorithm
Artificial intelligence
lip contour
support vector machine
User interface design & usability
Online AccessGet full text
ISBN9783642271717
3642271715
ISSN0302-9743
1611-3349
DOI10.1007/978-3-642-27172-4_72

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Abstract This paper provides an alternative approach to emotion recognition from the outer lip-contour of the subjects. Subjects exhibit their emotions through their facial expressions, and the lip region is segmented from their facial images. A lip-contour model has been developed to represent the boundary of the lip, and the parameters of the model are adapted using artificial bee colony (ABC) optimization algorithm to match it with the boundary contour of the lip. An SVM classifier is then employed to classify the emotion of the subject from the parameter set of the subjects’ lip-contour. The experiment was performed on 50 subjects, and the average case accuracy in emotion classification is found to be 86%.
AbstractList This paper provides an alternative approach to emotion recognition from the outer lip-contour of the subjects. Subjects exhibit their emotions through their facial expressions, and the lip region is segmented from their facial images. A lip-contour model has been developed to represent the boundary of the lip, and the parameters of the model are adapted using artificial bee colony (ABC) optimization algorithm to match it with the boundary contour of the lip. An SVM classifier is then employed to classify the emotion of the subject from the parameter set of the subjects’ lip-contour. The experiment was performed on 50 subjects, and the average case accuracy in emotion classification is found to be 86%.
Author Rakshit, Pratyusha
Konar, Amit
Janarthanan, Ramadoss
Halder, Anisha
Chakraborty, Aruna
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Snippet This paper provides an alternative approach to emotion recognition from the outer lip-contour of the subjects. Subjects exhibit their emotions through their...
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StartPage 610
SubjectTerms Algorithms & data structures
artificial bee colony optimization algorithm
Artificial intelligence
lip contour
support vector machine
User interface design & usability
Title Emotion Recognition from the Lip-Contour of a Subject Using Artificial Bee Colony Optimization Algorithm
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