Diagonal Fisher linear discriminant analysis for efficient face recognition
In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector trans...
Gespeichert in:
| Veröffentlicht in: | Neurocomputing (Amsterdam) 2006-08, Vol.69 (13), p.1711-1716 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1716 |
|---|---|
| container_issue | 13 |
| container_start_page | 1711 |
| container_title | Neurocomputing (Amsterdam) |
| container_volume | 69 |
| creator | Noushath, S. Hemantha Kumar, G. Shivakumara, P. |
| description | In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods. |
| doi_str_mv | 10.1016/j.neucom.2006.01.012 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_19444963</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0925231206000877</els_id><sourcerecordid>19444963</sourcerecordid><originalsourceid>FETCH-LOGICAL-c337t-65e7d7891a2e36a0dd47e01189a5c535f6c033e7be9ca589d6b10949ffa36ec53</originalsourceid><addsrcrecordid>eNp9UMFKAzEUDKJgrf6Bhz152zUv2c1uLoJUq2LBi55Dmn2pKdukJlvBvzdlPQsDD96bGd4MIddAK6AgbreVx4MJu4pRKioKGeyEzKBrWdmxTpySGZWsKRkHdk4uUtpSCi0wOSOvD05vgtdDsXTpE2MxOI86Fr1LJrqd89qPhc73n-RSYUMs0FpnHOa11QaLiCZsvBtd8JfkzOoh4dXfnJOP5eP74rlcvT29LO5XpeG8HUvRYNu3nQTNkAtN-75ukQJ0Ujem4Y0VhnKO7Rql0U0ne7EGKmtpreYCM2NObibffQxfB0yj2uVvcRi0x3BICmRd11LwTKwnookhpYhW7XMmHX8UUHVsTm3V1Jw6NqcoZLAsu5tkmEN8O4wqHQMb7F1OO6o-uP8NfgGXnXns</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>19444963</pqid></control><display><type>article</type><title>Diagonal Fisher linear discriminant analysis for efficient face recognition</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Noushath, S. ; Hemantha Kumar, G. ; Shivakumara, P.</creator><creatorcontrib>Noushath, S. ; Hemantha Kumar, G. ; Shivakumara, P.</creatorcontrib><description>In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods.</description><identifier>ISSN: 0925-2312</identifier><identifier>EISSN: 1872-8286</identifier><identifier>DOI: 10.1016/j.neucom.2006.01.012</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>2-Dimensional FLD ; 2-Dimensional PCA ; Diagonal FLD ; Face recognition ; Fisher linear discriminant analysis (FLD) ; Object recognition ; Principal component analysis (PCA)</subject><ispartof>Neurocomputing (Amsterdam), 2006-08, Vol.69 (13), p.1711-1716</ispartof><rights>2006 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-65e7d7891a2e36a0dd47e01189a5c535f6c033e7be9ca589d6b10949ffa36ec53</citedby><cites>FETCH-LOGICAL-c337t-65e7d7891a2e36a0dd47e01189a5c535f6c033e7be9ca589d6b10949ffa36ec53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.neucom.2006.01.012$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Noushath, S.</creatorcontrib><creatorcontrib>Hemantha Kumar, G.</creatorcontrib><creatorcontrib>Shivakumara, P.</creatorcontrib><title>Diagonal Fisher linear discriminant analysis for efficient face recognition</title><title>Neurocomputing (Amsterdam)</title><description>In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods.</description><subject>2-Dimensional FLD</subject><subject>2-Dimensional PCA</subject><subject>Diagonal FLD</subject><subject>Face recognition</subject><subject>Fisher linear discriminant analysis (FLD)</subject><subject>Object recognition</subject><subject>Principal component analysis (PCA)</subject><issn>0925-2312</issn><issn>1872-8286</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNp9UMFKAzEUDKJgrf6Bhz152zUv2c1uLoJUq2LBi55Dmn2pKdukJlvBvzdlPQsDD96bGd4MIddAK6AgbreVx4MJu4pRKioKGeyEzKBrWdmxTpySGZWsKRkHdk4uUtpSCi0wOSOvD05vgtdDsXTpE2MxOI86Fr1LJrqd89qPhc73n-RSYUMs0FpnHOa11QaLiCZsvBtd8JfkzOoh4dXfnJOP5eP74rlcvT29LO5XpeG8HUvRYNu3nQTNkAtN-75ukQJ0Ujem4Y0VhnKO7Rql0U0ne7EGKmtpreYCM2NObibffQxfB0yj2uVvcRi0x3BICmRd11LwTKwnookhpYhW7XMmHX8UUHVsTm3V1Jw6NqcoZLAsu5tkmEN8O4wqHQMb7F1OO6o-uP8NfgGXnXns</recordid><startdate>20060801</startdate><enddate>20060801</enddate><creator>Noushath, S.</creator><creator>Hemantha Kumar, G.</creator><creator>Shivakumara, P.</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20060801</creationdate><title>Diagonal Fisher linear discriminant analysis for efficient face recognition</title><author>Noushath, S. ; Hemantha Kumar, G. ; Shivakumara, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-65e7d7891a2e36a0dd47e01189a5c535f6c033e7be9ca589d6b10949ffa36ec53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>2-Dimensional FLD</topic><topic>2-Dimensional PCA</topic><topic>Diagonal FLD</topic><topic>Face recognition</topic><topic>Fisher linear discriminant analysis (FLD)</topic><topic>Object recognition</topic><topic>Principal component analysis (PCA)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Noushath, S.</creatorcontrib><creatorcontrib>Hemantha Kumar, G.</creatorcontrib><creatorcontrib>Shivakumara, P.</creatorcontrib><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Neurocomputing (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Noushath, S.</au><au>Hemantha Kumar, G.</au><au>Shivakumara, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diagonal Fisher linear discriminant analysis for efficient face recognition</atitle><jtitle>Neurocomputing (Amsterdam)</jtitle><date>2006-08-01</date><risdate>2006</risdate><volume>69</volume><issue>13</issue><spage>1711</spage><epage>1716</epage><pages>1711-1716</pages><issn>0925-2312</issn><eissn>1872-8286</eissn><abstract>In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.neucom.2006.01.012</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0925-2312 |
| ispartof | Neurocomputing (Amsterdam), 2006-08, Vol.69 (13), p.1711-1716 |
| issn | 0925-2312 1872-8286 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_19444963 |
| source | Elsevier ScienceDirect Journals Complete |
| subjects | 2-Dimensional FLD 2-Dimensional PCA Diagonal FLD Face recognition Fisher linear discriminant analysis (FLD) Object recognition Principal component analysis (PCA) |
| title | Diagonal Fisher linear discriminant analysis for efficient face recognition |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T06%3A19%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Diagonal%20Fisher%20linear%20discriminant%20analysis%20for%20efficient%20face%20recognition&rft.jtitle=Neurocomputing%20(Amsterdam)&rft.au=Noushath,%20S.&rft.date=2006-08-01&rft.volume=69&rft.issue=13&rft.spage=1711&rft.epage=1716&rft.pages=1711-1716&rft.issn=0925-2312&rft.eissn=1872-8286&rft_id=info:doi/10.1016/j.neucom.2006.01.012&rft_dat=%3Cproquest_cross%3E19444963%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=19444963&rft_id=info:pmid/&rft_els_id=S0925231206000877&rfr_iscdi=true |