Null space-based LDA with weighted dual personal subspaces for face recognition
Linear discriminant analysis (LDA) is popular feature extraction technique for face recognition. However, it often suffers the small sample size problem when dealing with the high dimensional face data. Moreover, the within-class and between-class scatter matrix used in LDA have low effective when d...
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| creator | Xipeng Qiu Lide Wu |
| description | Linear discriminant analysis (LDA) is popular feature extraction technique for face recognition. However, it often suffers the small sample size problem when dealing with the high dimensional face data. Moreover, the within-class and between-class scatter matrix used in LDA have low effective when dealing with face data of non-Gaussian density. In this paper, we propose a new method for face recognition. We first calculate the weighted dual personal subspaces to replace the within and between class matrix, then null space-based LDA is performed. The experiments show our method outperforms existing LDA and state-of-art face recognition approaches. |
| doi_str_mv | 10.1109/ICIP.2005.1530210 |
| format | Conference Proceeding |
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However, it often suffers the small sample size problem when dealing with the high dimensional face data. Moreover, the within-class and between-class scatter matrix used in LDA have low effective when dealing with face data of non-Gaussian density. In this paper, we propose a new method for face recognition. We first calculate the weighted dual personal subspaces to replace the within and between class matrix, then null space-based LDA is performed. The experiments show our method outperforms existing LDA and state-of-art face recognition approaches.</description><subject>Computer science</subject><subject>Covariance matrix</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>Linear discriminant analysis</subject><subject>Null space</subject><subject>Principal component analysis</subject><subject>Scattering</subject><subject>Training data</subject><subject>Vectors</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>9780780391345</isbn><isbn>0780391349</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotUMlqwzAUFF2gIfUHlF70A06flmdLx5BugdD0kHvQ8pyouHGwHEL_vqbNMDDDMMxhGHsQMBMC7NNysfycSQCcCVQgBVyxiVRGlAa1vWaFrQ2MVFYojTdsIlDKUhsDd6zI-QtGaNRQ1RO2_ji1Lc9HF6j0LlPkq-c5P6dhz8-UdvthTOLJtfxIfe4Oo8kn_1fPvOl63oyO9xS63SENqTvcs9vGtZmKi07Z5vVls3gvV-u35WK-KpOFoQyBqDIKIqJCBG-lCTF6EuRjqCNIgxVUldNBCxsMOLTkPTaeNIWgg5qyx__ZRETbY5--Xf-zvbyhfgGGjVHz</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Xipeng Qiu</creator><creator>Lide Wu</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2005</creationdate><title>Null space-based LDA with weighted dual personal subspaces for face recognition</title><author>Xipeng Qiu ; Lide Wu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-ccee6830d553550b928cddbe1ebdc7d02856066a4c419c80a59ebb5fbe4ecc4c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Computer science</topic><topic>Covariance matrix</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>Linear discriminant analysis</topic><topic>Null space</topic><topic>Principal component analysis</topic><topic>Scattering</topic><topic>Training data</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Xipeng Qiu</creatorcontrib><creatorcontrib>Lide Wu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xipeng Qiu</au><au>Lide Wu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Null space-based LDA with weighted dual personal subspaces for face recognition</atitle><btitle>IEEE International Conference on Image Processing 2005</btitle><stitle>ICIP</stitle><date>2005</date><risdate>2005</risdate><volume>2</volume><spage>II</spage><epage>934</epage><pages>II-934</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>9780780391345</isbn><isbn>0780391349</isbn><abstract>Linear discriminant analysis (LDA) is popular feature extraction technique for face recognition. However, it often suffers the small sample size problem when dealing with the high dimensional face data. Moreover, the within-class and between-class scatter matrix used in LDA have low effective when dealing with face data of non-Gaussian density. In this paper, we propose a new method for face recognition. We first calculate the weighted dual personal subspaces to replace the within and between class matrix, then null space-based LDA is performed. The experiments show our method outperforms existing LDA and state-of-art face recognition approaches.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2005.1530210</doi></addata></record> |
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| issn | 1522-4880 2381-8549 |
| language | eng |
| recordid | cdi_ieee_primary_1530210 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computer science Covariance matrix Face recognition Feature extraction Linear discriminant analysis Null space Principal component analysis Scattering Training data Vectors |
| title | Null space-based LDA with weighted dual personal subspaces for face recognition |
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