DocumentCode
3036681
Title
Japanese Face Emotions Classification Using LIP Features
Author
Rizon, Mohamed ; Karthigayan, M. ; Yaacob, Sazali ; Nagarajan, R.
Author_Institution
Univ. Malaysia Perlis, Jejawi
fYear
2007
fDate
4-6 July 2007
Firstpage
140
Lastpage
144
Abstract
In this paper, lip features are applied to classify the human emotion using a set of irregular ellipse fitting equations using Genetic algorithm. As Japanese, is considered in this study. All six universally accepted emotions are considered for classifications. Lip is usually considered as one of the features for recognizing the emotion. In this work, three feature extraction methods are proposed and their respective performances are compared for determining the feature of the lips. The method which is fastest in extracting lip features is adopted in this study. Observation of various emotions of the subject lead to unique characteristic of lips. GA is adopted to optimize such irregular ellipse characteristics of the lip features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotion. This has given reasonably successful emotion classifications for Japanese subject.
Keywords
emotion recognition; face recognition; feature extraction; genetic algorithms; image classification; Japanese face emotions classification; feature extraction methods; fitness equations; genetic algorithm; irregular ellipse fitting equations; lip features; Emotion recognition; Equations; Face detection; Face recognition; Feature extraction; Genetic algorithms; Humans; Image processing; Lips; Neural networks; Face emotion recognition.; Feature extraction; Genetic; Irregular ellipse fitness function; algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Geometric Modeling and Imaging, 2007. GMAI '07
Conference_Location
Zurich
Print_ISBN
0-7695-2901-1
Type
conf
DOI
10.1109/GMAI.2007.24
Filename
4271734
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