Recognition of Facial Expression Using Centroid Neural Network
A novel approach to recognize facial expressions from static images is proposed in this paper. The local binary pattern (LBP) operator is adopted as an effective feature extraction tool for facial image data. An unsupervised competitive neural network, called a centroid neural network with x2 distan...
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| creator | Dong-Chul Park Huynh Thuy Dong-Min Woo Yunsik Lee |
| description | A novel approach to recognize facial expressions from static images is proposed in this paper. The local binary pattern (LBP) operator is adopted as an effective feature extraction tool for facial image data. An unsupervised competitive neural network, called a centroid neural network with x2 distance measure, CNN-x2, is then utilized as the classification tool for the histogram data obtained by the LBP operator on facial image data. The proposed recognition scheme is applied to the JAFFE database and compared with several conventional approaches to facial expression recognition problems. The results show that the proposed recognition scheme compares favorably with conventional approaches in terms of recognition accuracy. |
| doi_str_mv | 10.1109/CyberC.2010.94 |
| format | Conference Proceeding |
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The local binary pattern (LBP) operator is adopted as an effective feature extraction tool for facial image data. An unsupervised competitive neural network, called a centroid neural network with x2 distance measure, CNN-x2, is then utilized as the classification tool for the histogram data obtained by the LBP operator on facial image data. The proposed recognition scheme is applied to the JAFFE database and compared with several conventional approaches to facial expression recognition problems. The results show that the proposed recognition scheme compares favorably with conventional approaches in terms of recognition accuracy.</description><identifier>ISBN: 1424484340</identifier><identifier>ISBN: 9781424484348</identifier><identifier>EISBN: 9780769542355</identifier><identifier>EISBN: 0769542352</identifier><identifier>DOI: 10.1109/CyberC.2010.94</identifier><language>eng</language><publisher>IEEE</publisher><subject>Clustering algorithms ; Face ; Face recognition ; facial expression ; Feature extraction ; Histograms ; neural network ; Pixel ; recognition</subject><ispartof>2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2010, p.480-485</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5616993$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5616993$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Dong-Chul Park</creatorcontrib><creatorcontrib>Huynh Thuy</creatorcontrib><creatorcontrib>Dong-Min Woo</creatorcontrib><creatorcontrib>Yunsik Lee</creatorcontrib><title>Recognition of Facial Expression Using Centroid Neural Network</title><title>2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery</title><addtitle>cyberc</addtitle><description>A novel approach to recognize facial expressions from static images is proposed in this paper. The local binary pattern (LBP) operator is adopted as an effective feature extraction tool for facial image data. An unsupervised competitive neural network, called a centroid neural network with x2 distance measure, CNN-x2, is then utilized as the classification tool for the histogram data obtained by the LBP operator on facial image data. The proposed recognition scheme is applied to the JAFFE database and compared with several conventional approaches to facial expression recognition problems. The results show that the proposed recognition scheme compares favorably with conventional approaches in terms of recognition accuracy.</description><subject>Clustering algorithms</subject><subject>Face</subject><subject>Face recognition</subject><subject>facial expression</subject><subject>Feature extraction</subject><subject>Histograms</subject><subject>neural network</subject><subject>Pixel</subject><subject>recognition</subject><isbn>1424484340</isbn><isbn>9781424484348</isbn><isbn>9780769542355</isbn><isbn>0769542352</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjF1LwzAYhSMiqLO33njTP9CZjzdJ3xtByqbCmCDzeqTNmxGd7Ugqun9vRQ88HHg4HMauBZ8LwfG2ObaUmrnkk0A4YQXamluDGqTS-pRdCpAANSjg56zI-Y1PAS2l1Bfs7oW6YdfHMQ59OYRy6bro9uXi-5Ao51_5mmO_KxvqxzREX67pM02DNY1fQ3q_YmfB7TMV_z1jm-Vi0zxWq-eHp-Z-VUXkY2Xb2uq2NU5oC8Er5yBw4Y3XrUXnBRIG4QNAQA9GADfeoFEkJ5B3XM3Yzd9tJKLtIcUPl45bbYRBVOoHxIlJXQ</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Dong-Chul Park</creator><creator>Huynh Thuy</creator><creator>Dong-Min Woo</creator><creator>Yunsik Lee</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Recognition of Facial Expression Using Centroid Neural Network</title><author>Dong-Chul Park ; Huynh Thuy ; Dong-Min Woo ; Yunsik Lee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-7b875bb6a1574fd3aa4f01d6d5b79ad19e9f1df44f9d461406d6963e263e90c03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Clustering algorithms</topic><topic>Face</topic><topic>Face recognition</topic><topic>facial expression</topic><topic>Feature extraction</topic><topic>Histograms</topic><topic>neural network</topic><topic>Pixel</topic><topic>recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Dong-Chul Park</creatorcontrib><creatorcontrib>Huynh Thuy</creatorcontrib><creatorcontrib>Dong-Min Woo</creatorcontrib><creatorcontrib>Yunsik Lee</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dong-Chul Park</au><au>Huynh Thuy</au><au>Dong-Min Woo</au><au>Yunsik Lee</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Recognition of Facial Expression Using Centroid Neural Network</atitle><btitle>2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery</btitle><stitle>cyberc</stitle><date>2010-10</date><risdate>2010</risdate><spage>480</spage><epage>485</epage><pages>480-485</pages><isbn>1424484340</isbn><isbn>9781424484348</isbn><eisbn>9780769542355</eisbn><eisbn>0769542352</eisbn><abstract>A novel approach to recognize facial expressions from static images is proposed in this paper. The local binary pattern (LBP) operator is adopted as an effective feature extraction tool for facial image data. An unsupervised competitive neural network, called a centroid neural network with x2 distance measure, CNN-x2, is then utilized as the classification tool for the histogram data obtained by the LBP operator on facial image data. The proposed recognition scheme is applied to the JAFFE database and compared with several conventional approaches to facial expression recognition problems. The results show that the proposed recognition scheme compares favorably with conventional approaches in terms of recognition accuracy.</abstract><pub>IEEE</pub><doi>10.1109/CyberC.2010.94</doi><tpages>6</tpages></addata></record> |
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| subjects | Clustering algorithms Face Face recognition facial expression Feature extraction Histograms neural network Pixel recognition |
| title | Recognition of Facial Expression Using Centroid Neural Network |
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