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<Article>
<Journal>
				<PublisherName>Shahid Rajaee Teacher Training University</PublisherName>
				<JournalTitle>Journal of Electrical and Computer Engineering Innovations (JECEI)</JournalTitle>
				<Issn>2322-3952</Issn>
				<Volume>2</Volume>
				<Issue>2</Issue>
				<PubDate PubStatus="epublish">
					<Year>2014</Year>
					<Month>07</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Facial Expression Recognition Based on Anatomical Structure of Human Face</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>77</FirstPage>
			<LastPage>83</LastPage>
			<ELocationID EIdType="pii">245</ELocationID>
			
<ELocationID EIdType="doi">10.22061/jecei.2014.245</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>S.</FirstName>
					<LastName>Mohseni</LastName>
<Affiliation>Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology</Affiliation>

</Author>
<Author>
					<FirstName>G.</FirstName>
					<LastName>Ardeshir</LastName>
<Affiliation>Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology</Affiliation>

</Author>
<Author>
					<FirstName>N.</FirstName>
					<LastName>Zarei</LastName>
<Affiliation>Faculty of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2014</Year>
					<Month>05</Month>
					<Day>28</Day>
				</PubDate>
			</History>
		<Abstract>Automatic analysis of human facial expressions is one of the challenging problems in machine vision systems. It has many applications in human-computer interactions such as, social signal processing, social robots, deceit detection, interactive video and behavior monitoring. In this paper, we develop a new method for automatic facial expression recognition based on facial muscle anatomy and human face structure. The algorithm finds approximate location of effective facial muscles and extracts features by measuring skin texture in 11 local patches. Seven facial expressions, including neutral are being classified in this study using AdaBoost classifier and other classifiers on MMI databases. Experimental results show that analyzing skin texture from selected local patches gives accurate and efficient information in order to identify different facial expressions.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Facial expression analysis</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Human facial anatomy</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Adaboost classifier</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Support vector machine</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jecei.sru.ac.ir/article_245_681e2b1df065d69db5fe5ba1b5cefa67.pdf</ArchiveCopySource>
</Article>
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