Kalman Filtering for Pose-Invariant Face Recognition
We propose a novel algorithm for the identification of faces from image samples. The algorithm uses the Kalman filter to identify significant facial traits. Kalmanfaces are compact visual models that represent the invariant proportions of face classes. We employ the Kalmanfaces approach on the UMIST...
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| creator | Eidenberger, H. |
| description | We propose a novel algorithm for the identification of faces from image samples. The algorithm uses the Kalman filter to identify significant facial traits. Kalmanfaces are compact visual models that represent the invariant proportions of face classes. We employ the Kalmanfaces approach on the UMIST database, a collection of face images that were recorded under varying camera angles. Kalmanfaces show robustness against invisible facial traits and outperform the classic eigenfaces approach in terms of identification performance and algorithm speed. The paper discusses Kalmanfaces extraction, application, tunable parameters, experimental results and related work on Kalman filter application in face recognition. |
| doi_str_mv | 10.1109/ICIP.2006.312857 |
| format | Conference Proceeding |
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The algorithm uses the Kalman filter to identify significant facial traits. Kalmanfaces are compact visual models that represent the invariant proportions of face classes. We employ the Kalmanfaces approach on the UMIST database, a collection of face images that were recorded under varying camera angles. Kalmanfaces show robustness against invisible facial traits and outperform the classic eigenfaces approach in terms of identification performance and algorithm speed. The paper discusses Kalmanfaces extraction, application, tunable parameters, experimental results and related work on Kalman filter application in face recognition.</description><identifier>ISSN: 1522-4880</identifier><identifier>ISBN: 9781424404803</identifier><identifier>ISBN: 1424404800</identifier><identifier>EISSN: 2381-8549</identifier><identifier>EISBN: 9781424404810</identifier><identifier>EISBN: 1424404819</identifier><identifier>DOI: 10.1109/ICIP.2006.312857</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cameras ; Data mining ; Face detection ; Face recognition ; Facerecognition ; Feature extraction ; Filtering algorithms ; Image databases ; Kalman filters ; Kalmanfiltering ; Robustness ; Streaming media</subject><ispartof>2006 International Conference on Image Processing, 2006, p.2037-2040</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/4106960$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4106960$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Eidenberger, H.</creatorcontrib><title>Kalman Filtering for Pose-Invariant Face Recognition</title><title>2006 International Conference on Image Processing</title><addtitle>ICIP</addtitle><description>We propose a novel algorithm for the identification of faces from image samples. The algorithm uses the Kalman filter to identify significant facial traits. Kalmanfaces are compact visual models that represent the invariant proportions of face classes. We employ the Kalmanfaces approach on the UMIST database, a collection of face images that were recorded under varying camera angles. Kalmanfaces show robustness against invisible facial traits and outperform the classic eigenfaces approach in terms of identification performance and algorithm speed. The paper discusses Kalmanfaces extraction, application, tunable parameters, experimental results and related work on Kalman filter application in face recognition.</description><subject>Cameras</subject><subject>Data mining</subject><subject>Face detection</subject><subject>Face recognition</subject><subject>Facerecognition</subject><subject>Feature extraction</subject><subject>Filtering algorithms</subject><subject>Image databases</subject><subject>Kalman filters</subject><subject>Kalmanfiltering</subject><subject>Robustness</subject><subject>Streaming media</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>9781424404803</isbn><isbn>1424404800</isbn><isbn>9781424404810</isbn><isbn>1424404819</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVzLtKxEAUgOHxBsZ1e8EmLzDxnDP3UoKrwQUX0XqZJDPLSHYiSRB8ewttrP7ig5-xG4QKEdxdUze7igB0JZCsMids7YxFSVKCtAinrCBhkVsl3dk_A3HOClREXFoLl-xqnj8ACFBgweSzH44-l5s0LGFK-VDGcSp34xx4k7_8lHxeyo3vQvkauvGQ05LGfM0uoh_msP7rir1vHt7qJ759eWzq-y1PaNTC2950oTdK21ZGIpK96rzse2vJGyAVjPZK6VYEp7wS4EykiEC6jTFSJ8SK3f5-Uwhh_zmlo5--9xJBOw3iB1hrSRo</recordid><startdate>200610</startdate><enddate>200610</enddate><creator>Eidenberger, H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200610</creationdate><title>Kalman Filtering for Pose-Invariant Face Recognition</title><author>Eidenberger, H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-bd7ced7568b4f2224d5ca4dd882a7025e76a556b3e95a53097f2f1026bfff2c33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Cameras</topic><topic>Data mining</topic><topic>Face detection</topic><topic>Face recognition</topic><topic>Facerecognition</topic><topic>Feature extraction</topic><topic>Filtering algorithms</topic><topic>Image databases</topic><topic>Kalman filters</topic><topic>Kalmanfiltering</topic><topic>Robustness</topic><topic>Streaming media</topic><toplevel>online_resources</toplevel><creatorcontrib>Eidenberger, H.</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>Eidenberger, H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Kalman Filtering for Pose-Invariant Face Recognition</atitle><btitle>2006 International Conference on Image Processing</btitle><stitle>ICIP</stitle><date>2006-10</date><risdate>2006</risdate><spage>2037</spage><epage>2040</epage><pages>2037-2040</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>9781424404803</isbn><isbn>1424404800</isbn><eisbn>9781424404810</eisbn><eisbn>1424404819</eisbn><abstract>We propose a novel algorithm for the identification of faces from image samples. The algorithm uses the Kalman filter to identify significant facial traits. Kalmanfaces are compact visual models that represent the invariant proportions of face classes. We employ the Kalmanfaces approach on the UMIST database, a collection of face images that were recorded under varying camera angles. Kalmanfaces show robustness against invisible facial traits and outperform the classic eigenfaces approach in terms of identification performance and algorithm speed. The paper discusses Kalmanfaces extraction, application, tunable parameters, experimental results and related work on Kalman filter application in face recognition.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2006.312857</doi><tpages>4</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Cameras Data mining Face detection Face recognition Facerecognition Feature extraction Filtering algorithms Image databases Kalman filters Kalmanfiltering Robustness Streaming media |
| title | Kalman Filtering for Pose-Invariant Face Recognition |
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