Identification of human faces through texture-based feature recognition and neural network technology
A method is presented to infer the presence of a human face in an image through the identification of face-like textures. The selected textures are those of human hair and skin. The second-order statistics method is used for texture representation. This method employs a set of co-occurrence matrices...
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| container_end_page | 398 vol.1 |
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| creator | Augusteijn, M.F. Skufca, T.L. |
| description | A method is presented to infer the presence of a human face in an image through the identification of face-like textures. The selected textures are those of human hair and skin. The second-order statistics method is used for texture representation. This method employs a set of co-occurrence matrices, from which features can be calculated that can characterize a texture. The cascade-correlation neural network architecture is used for supervised classification of textures. The Kohonen self-organizing feature map shows the clustering of the different texture types. Classification performance is generally above 80%, which is sufficient to clearly outline a face in an image.< > |
| doi_str_mv | 10.1109/ICNN.1993.298589 |
| format | Conference Proceeding |
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The selected textures are those of human hair and skin. The second-order statistics method is used for texture representation. This method employs a set of co-occurrence matrices, from which features can be calculated that can characterize a texture. The cascade-correlation neural network architecture is used for supervised classification of textures. The Kohonen self-organizing feature map shows the clustering of the different texture types. 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The selected textures are those of human hair and skin. The second-order statistics method is used for texture representation. This method employs a set of co-occurrence matrices, from which features can be calculated that can characterize a texture. The cascade-correlation neural network architecture is used for supervised classification of textures. The Kohonen self-organizing feature map shows the clustering of the different texture types. Classification performance is generally above 80%, which is sufficient to clearly outline a face in an image.< ></description><subject>Computer science</subject><subject>Digital images</subject><subject>Face detection</subject><subject>Face recognition</subject><subject>Hair</subject><subject>Humans</subject><subject>Neural networks</subject><subject>Skin</subject><subject>Springs</subject><subject>Statistics</subject><isbn>0780309995</isbn><isbn>9780780309999</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1993</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkE1LxDAQhgMiqOvexVP-QGvSbLbJUYofC8t60fOSTidttJtImqL7742uc3mYF55heAm54azknOm7TbPblVxrUVZaSaXPyBWrFRNMay0vyHKa3lmeleRsVV0S3HTok7MOTHLB02DpMB-Mp9YATjQNMcz9QBN-pzli0ZoJO2rR_G40IoTeuz_R-I56nKMZM9JXiB9ZgsGHMfTHa3JuzTjh8p8L8vb48No8F9uXp01zvy0cr1kqWqjEWsIaBGrdWsjvM4OInEEngIOsZY7biimQrZLCKF6DUJYZULKrhViQ29Ndl639Z3QHE4_7UxHiB6WrVsU</recordid><startdate>1993</startdate><enddate>1993</enddate><creator>Augusteijn, M.F.</creator><creator>Skufca, T.L.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1993</creationdate><title>Identification of human faces through texture-based feature recognition and neural network technology</title><author>Augusteijn, M.F. ; Skufca, T.L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i170t-bc2365c6c3e99bfc5890aeee10cd3c1c5759bfb208c5b853a817c38f0ac85d733</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng ; jpn</language><creationdate>1993</creationdate><topic>Computer science</topic><topic>Digital images</topic><topic>Face detection</topic><topic>Face recognition</topic><topic>Hair</topic><topic>Humans</topic><topic>Neural networks</topic><topic>Skin</topic><topic>Springs</topic><topic>Statistics</topic><toplevel>online_resources</toplevel><creatorcontrib>Augusteijn, M.F.</creatorcontrib><creatorcontrib>Skufca, T.L.</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</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>Augusteijn, M.F.</au><au>Skufca, T.L.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Identification of human faces through texture-based feature recognition and neural network technology</atitle><btitle>IEEE International Conference on Neural Networks</btitle><stitle>ICNN</stitle><date>1993</date><risdate>1993</risdate><spage>392</spage><epage>398 vol.1</epage><pages>392-398 vol.1</pages><isbn>0780309995</isbn><isbn>9780780309999</isbn><abstract>A method is presented to infer the presence of a human face in an image through the identification of face-like textures. The selected textures are those of human hair and skin. The second-order statistics method is used for texture representation. This method employs a set of co-occurrence matrices, from which features can be calculated that can characterize a texture. The cascade-correlation neural network architecture is used for supervised classification of textures. The Kohonen self-organizing feature map shows the clustering of the different texture types. Classification performance is generally above 80%, which is sufficient to clearly outline a face in an image.< ></abstract><pub>IEEE</pub><doi>10.1109/ICNN.1993.298589</doi></addata></record> |
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| identifier | ISBN: 0780309995 |
| ispartof | IEEE International Conference on Neural Networks, 1993, p.392-398 vol.1 |
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| language | eng ; jpn |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computer science Digital images Face detection Face recognition Hair Humans Neural networks Skin Springs Statistics |
| title | Identification of human faces through texture-based feature recognition and neural network technology |
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