Dynamic Local Feature Analysis for Face Recognition
This paper introduces an innovative method, Dynamic Local Feature Analysis (DLFA), for human face recognition. In our proposed method, the face shape and the facial texture information are combined together by using the Local Feature Analysis (LFA) technique. The shape information is obtained by usi...
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| creator | Ng, Johnny Cheung, Humphrey |
| description | This paper introduces an innovative method, Dynamic Local Feature Analysis (DLFA), for human face recognition. In our proposed method, the face shape and the facial texture information are combined together by using the Local Feature Analysis (LFA) technique. The shape information is obtained by using our proposed adaptive edge detecting method that can reduce the effect on different lighting conditions, while the texture information provides the details of the normalized facial feature on the image. Finally, both the shape and texture information is combined together by means of LFA for dimension reduction. As a result, a high recognition rate is achieved no matter the face is enrolled under different or bad lighting conditions. |
| doi_str_mv | 10.1007/978-3-540-25948-0_33 |
| format | Book Chapter |
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In our proposed method, the face shape and the facial texture information are combined together by using the Local Feature Analysis (LFA) technique. The shape information is obtained by using our proposed adaptive edge detecting method that can reduce the effect on different lighting conditions, while the texture information provides the details of the normalized facial feature on the image. Finally, both the shape and texture information is combined together by means of LFA for dimension reduction. As a result, a high recognition rate is achieved no matter the face is enrolled under different or bad lighting conditions.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540221463</identifier><identifier>ISBN: 3540221468</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540259480</identifier><identifier>EISBN: 3540259481</identifier><identifier>DOI: 10.1007/978-3-540-25948-0_33</identifier><identifier>OCLC: 934980641</identifier><identifier>LCCallNum: Q337.5</identifier><language>eng</language><publisher>Germany: Springer Berlin / Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Face Database ; Face Image ; Face Recognition ; High Recognition Rate ; Pattern recognition. Digital image processing. 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In our proposed method, the face shape and the facial texture information are combined together by using the Local Feature Analysis (LFA) technique. The shape information is obtained by using our proposed adaptive edge detecting method that can reduce the effect on different lighting conditions, while the texture information provides the details of the normalized facial feature on the image. Finally, both the shape and texture information is combined together by means of LFA for dimension reduction. As a result, a high recognition rate is achieved no matter the face is enrolled under different or bad lighting conditions.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Face Database</subject><subject>Face Image</subject><subject>Face Recognition</subject><subject>High Recognition Rate</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Recognition Rate</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540221463</isbn><isbn>3540221468</isbn><isbn>9783540259480</isbn><isbn>3540259481</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2004</creationdate><recordtype>book_chapter</recordtype><recordid>eNpFkM1OwzAQhM2viErfgEMuHA1er5PYx6pQQKqEhOBsOa5dAmlS7HDo2-O0lfBhLc3OrL0fITfA7oCx6l5VkiItBKO8UEJSphFPyDTJmMS9xk5JBiUARRTq7L_HQZR4TjKGjFNVCbwkmUoWyUoBV2Qa4xdLB4CDKjKCD7vObBqbL3tr2nzhzPAbXD7rTLuLTcx9H_KFsS5_c7Zfd83Q9N01ufCmjW56vCfkY_H4Pn-my9enl_lsSS1yHKh1WABX6WVluS35ylgAZdB4tFXBU6nBC-mtkoJBwRCc9FjzFdQwboYTcnuYuzUxfc4H09km6m1oNibsNBRKoVJV8vGDL6ZWt3ZB133_HTUwPdLUCY1GneDoPTk90kwhPA4P_c-vi4N2Y8q6bgimtZ9mO7gQNTIpE0mNMoU5_gE7zW_Z</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Ng, Johnny</creator><creator>Cheung, Humphrey</creator><general>Springer Berlin / Heidelberg</general><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>FFUUA</scope><scope>IQODW</scope></search><sort><creationdate>2004</creationdate><title>Dynamic Local Feature Analysis for Face Recognition</title><author>Ng, Johnny ; Cheung, Humphrey</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c323t-ce351299809c2c62dac119a3af3c7523c7b1f48fc984015031e8f3b2d1b116113</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Face Database</topic><topic>Face Image</topic><topic>Face Recognition</topic><topic>High Recognition Rate</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Recognition Rate</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ng, Johnny</creatorcontrib><creatorcontrib>Cheung, Humphrey</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ng, Johnny</au><au>Cheung, Humphrey</au><au>Jain, Anil K</au><au>Zhang, David Y</au><au>Jain, Anil K.</au><au>Zhang, David</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Dynamic Local Feature Analysis for Face Recognition</atitle><btitle>Biometric Authentication</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2004</date><risdate>2004</risdate><volume>3072</volume><spage>234</spage><epage>240</epage><pages>234-240</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540221463</isbn><isbn>3540221468</isbn><eisbn>9783540259480</eisbn><eisbn>3540259481</eisbn><abstract>This paper introduces an innovative method, Dynamic Local Feature Analysis (DLFA), for human face recognition. In our proposed method, the face shape and the facial texture information are combined together by using the Local Feature Analysis (LFA) technique. The shape information is obtained by using our proposed adaptive edge detecting method that can reduce the effect on different lighting conditions, while the texture information provides the details of the normalized facial feature on the image. Finally, both the shape and texture information is combined together by means of LFA for dimension reduction. As a result, a high recognition rate is achieved no matter the face is enrolled under different or bad lighting conditions.</abstract><cop>Germany</cop><pub>Springer Berlin / Heidelberg</pub><doi>10.1007/978-3-540-25948-0_33</doi><oclcid>934980641</oclcid><tpages>7</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Biometric Authentication, 2004, Vol.3072, p.234-240 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_15993997 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Face Database Face Image Face Recognition High Recognition Rate Pattern recognition. Digital image processing. Computational geometry Recognition Rate |
| title | Dynamic Local Feature Analysis for Face Recognition |
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