Facecut - a robust approach for facial feature segmentation
Segmentation of facial features is a key pre-processing step in enabling facial recognition, building of 3D facial models, expression analysis, and pose estimation. Recently, graph cuts based algorithms have been adapted to carry out this task but many of these methods require manual initialization...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1844 |
|---|---|
| container_issue | |
| container_start_page | 1841 |
| container_title | |
| container_volume | |
| creator | Khoa Luu Le, T. H. N. Seshadri, K. Savvides, M. |
| description | Segmentation of facial features is a key pre-processing step in enabling facial recognition, building of 3D facial models, expression analysis, and pose estimation. Recently, graph cuts based algorithms have been adapted to carry out this task but many of these methods require manual initialization of points in the foreground and background. In this paper, we propose a novel and fully automatic approach, named Face-Cut, to perform accurate facial feature segmentation. FaceCut combines the positive features of the Modified Active Shape Model (MASM) and GrowCut algorithms to ensure highly accurate and completely automatic segmentation of facial features. We demonstrate the effectiveness of FaceCut on images from two challenging databases. |
| doi_str_mv | 10.1109/ICIP.2012.6467241 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6467241</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6467241</ieee_id><sourcerecordid>6467241</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-48c096cea728744cedadb02510dc4b75ebbafd6caea776781e5151171646ed9c3</originalsourceid><addsrcrecordid>eNo1UM1Kw0AYXP_AtPYBxMu-QOJ--5Pd4EmK1UBBD3ouXzZfNNI2YbM5-PYuWE_DMMMMM4zdgigARHVfr-u3QgqQRalLKzWcsVVlHSSipFFKnrNMKge5M7q6YIt_QcMly8BImWvnxDVbTNO3EClIQcYeNujJz5HnHHkYmnmKHMcxDOi_eDcE3qHvcc87wjgH4hN9HugYMfbD8YZddbifaHXCJfvYPL2vX_Lt63O9ftzmPVgTU6sXVekJrXRWa08tto2QBkTrdWMNNQ12bekxOWyZBpEBA2AhzaS28mrJ7v5yeyLajaE_YPjZnU5Qv1Q_S6o</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Facecut - a robust approach for facial feature segmentation</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Khoa Luu ; Le, T. H. N. ; Seshadri, K. ; Savvides, M.</creator><creatorcontrib>Khoa Luu ; Le, T. H. N. ; Seshadri, K. ; Savvides, M.</creatorcontrib><description>Segmentation of facial features is a key pre-processing step in enabling facial recognition, building of 3D facial models, expression analysis, and pose estimation. Recently, graph cuts based algorithms have been adapted to carry out this task but many of these methods require manual initialization of points in the foreground and background. In this paper, we propose a novel and fully automatic approach, named Face-Cut, to perform accurate facial feature segmentation. FaceCut combines the positive features of the Modified Active Shape Model (MASM) and GrowCut algorithms to ensure highly accurate and completely automatic segmentation of facial features. We demonstrate the effectiveness of FaceCut on images from two challenging databases.</description><identifier>ISSN: 1522-4880</identifier><identifier>ISBN: 1467325341</identifier><identifier>ISBN: 9781467325349</identifier><identifier>EISSN: 2381-8549</identifier><identifier>EISBN: 9781467325332</identifier><identifier>EISBN: 1467325325</identifier><identifier>EISBN: 9781467325325</identifier><identifier>EISBN: 1467325333</identifier><identifier>DOI: 10.1109/ICIP.2012.6467241</identifier><language>eng</language><publisher>IEEE</publisher><subject>Active Shape Models (ASMs) ; Face ; Face segmentation ; FaceCut ; Facial features ; facial landmarks ; graph cuts ; Image color analysis ; Image segmentation ; Shape ; Skin</subject><ispartof>2012 19th IEEE International Conference on Image Processing, 2012, p.1841-1844</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/6467241$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2057,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6467241$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Khoa Luu</creatorcontrib><creatorcontrib>Le, T. H. N.</creatorcontrib><creatorcontrib>Seshadri, K.</creatorcontrib><creatorcontrib>Savvides, M.</creatorcontrib><title>Facecut - a robust approach for facial feature segmentation</title><title>2012 19th IEEE International Conference on Image Processing</title><addtitle>ICIP</addtitle><description>Segmentation of facial features is a key pre-processing step in enabling facial recognition, building of 3D facial models, expression analysis, and pose estimation. Recently, graph cuts based algorithms have been adapted to carry out this task but many of these methods require manual initialization of points in the foreground and background. In this paper, we propose a novel and fully automatic approach, named Face-Cut, to perform accurate facial feature segmentation. FaceCut combines the positive features of the Modified Active Shape Model (MASM) and GrowCut algorithms to ensure highly accurate and completely automatic segmentation of facial features. We demonstrate the effectiveness of FaceCut on images from two challenging databases.</description><subject>Active Shape Models (ASMs)</subject><subject>Face</subject><subject>Face segmentation</subject><subject>FaceCut</subject><subject>Facial features</subject><subject>facial landmarks</subject><subject>graph cuts</subject><subject>Image color analysis</subject><subject>Image segmentation</subject><subject>Shape</subject><subject>Skin</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>1467325341</isbn><isbn>9781467325349</isbn><isbn>9781467325332</isbn><isbn>1467325325</isbn><isbn>9781467325325</isbn><isbn>1467325333</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UM1Kw0AYXP_AtPYBxMu-QOJ--5Pd4EmK1UBBD3ouXzZfNNI2YbM5-PYuWE_DMMMMM4zdgigARHVfr-u3QgqQRalLKzWcsVVlHSSipFFKnrNMKge5M7q6YIt_QcMly8BImWvnxDVbTNO3EClIQcYeNujJz5HnHHkYmnmKHMcxDOi_eDcE3qHvcc87wjgH4hN9HugYMfbD8YZddbifaHXCJfvYPL2vX_Lt63O9ftzmPVgTU6sXVekJrXRWa08tto2QBkTrdWMNNQ12bekxOWyZBpEBA2AhzaS28mrJ7v5yeyLajaE_YPjZnU5Qv1Q_S6o</recordid><startdate>201209</startdate><enddate>201209</enddate><creator>Khoa Luu</creator><creator>Le, T. H. N.</creator><creator>Seshadri, K.</creator><creator>Savvides, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201209</creationdate><title>Facecut - a robust approach for facial feature segmentation</title><author>Khoa Luu ; Le, T. H. N. ; Seshadri, K. ; Savvides, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-48c096cea728744cedadb02510dc4b75ebbafd6caea776781e5151171646ed9c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Active Shape Models (ASMs)</topic><topic>Face</topic><topic>Face segmentation</topic><topic>FaceCut</topic><topic>Facial features</topic><topic>facial landmarks</topic><topic>graph cuts</topic><topic>Image color analysis</topic><topic>Image segmentation</topic><topic>Shape</topic><topic>Skin</topic><toplevel>online_resources</toplevel><creatorcontrib>Khoa Luu</creatorcontrib><creatorcontrib>Le, T. H. N.</creatorcontrib><creatorcontrib>Seshadri, K.</creatorcontrib><creatorcontrib>Savvides, M.</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>Khoa Luu</au><au>Le, T. H. N.</au><au>Seshadri, K.</au><au>Savvides, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Facecut - a robust approach for facial feature segmentation</atitle><btitle>2012 19th IEEE International Conference on Image Processing</btitle><stitle>ICIP</stitle><date>2012-09</date><risdate>2012</risdate><spage>1841</spage><epage>1844</epage><pages>1841-1844</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>1467325341</isbn><isbn>9781467325349</isbn><eisbn>9781467325332</eisbn><eisbn>1467325325</eisbn><eisbn>9781467325325</eisbn><eisbn>1467325333</eisbn><abstract>Segmentation of facial features is a key pre-processing step in enabling facial recognition, building of 3D facial models, expression analysis, and pose estimation. Recently, graph cuts based algorithms have been adapted to carry out this task but many of these methods require manual initialization of points in the foreground and background. In this paper, we propose a novel and fully automatic approach, named Face-Cut, to perform accurate facial feature segmentation. FaceCut combines the positive features of the Modified Active Shape Model (MASM) and GrowCut algorithms to ensure highly accurate and completely automatic segmentation of facial features. We demonstrate the effectiveness of FaceCut on images from two challenging databases.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2012.6467241</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1522-4880 |
| ispartof | 2012 19th IEEE International Conference on Image Processing, 2012, p.1841-1844 |
| issn | 1522-4880 2381-8549 |
| language | eng |
| recordid | cdi_ieee_primary_6467241 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Active Shape Models (ASMs) Face Face segmentation FaceCut Facial features facial landmarks graph cuts Image color analysis Image segmentation Shape Skin |
| title | Facecut - a robust approach for facial feature segmentation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T21%3A28%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Facecut%20-%20a%20robust%20approach%20for%20facial%20feature%20segmentation&rft.btitle=2012%2019th%20IEEE%20International%20Conference%20on%20Image%20Processing&rft.au=Khoa%20Luu&rft.date=2012-09&rft.spage=1841&rft.epage=1844&rft.pages=1841-1844&rft.issn=1522-4880&rft.eissn=2381-8549&rft.isbn=1467325341&rft.isbn_list=9781467325349&rft_id=info:doi/10.1109/ICIP.2012.6467241&rft_dat=%3Cieee_6IE%3E6467241%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781467325332&rft.eisbn_list=1467325325&rft.eisbn_list=9781467325325&rft.eisbn_list=1467325333&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6467241&rfr_iscdi=true |