BEMD for expression transformation in face recognition
This work presents a novel methodology for the transformation of facial expressions, to assist face biometrics. It is known that identification using only one image per subject poses a great challenge to recognizers. This is because drastic facial expressions introduce variability, on which the reco...
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 | 1504 |
|---|---|
| container_issue | |
| container_start_page | 1501 |
| container_title | |
| container_volume | |
| creator | Mohammadzade, Hoda Agrafioti, Foteini Jiexin Gao Hatzinakos, Dimitrios |
| description | This work presents a novel methodology for the transformation of facial expressions, to assist face biometrics. It is known that identification using only one image per subject poses a great challenge to recognizers. This is because drastic facial expressions introduce variability, on which the recognizer is not trained. The proposed framework uses only one image per subject to predict intra-class variability, by synthesizing new expressions, which are subsequently used to train the discriminant. The expression of the gallery is transformed using the bivariate empirical mode decomposition (BEMD), which allows for simultaneous analysis of the probe image and a targeted expression mask. We advocate that 2D BEMD is a powerful tool for multi-resolution face analysis. The performance of the proposed framework, tested over a database of 96 individuals, is 90% for an FAR of 1%. |
| doi_str_mv | 10.1109/ICASSP.2011.5946778 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5946778</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5946778</ieee_id><sourcerecordid>5946778</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-d86c2f46bdbb450556b0315ed67d2eb0e1a17aeb334329ed254ac66c04f75143</originalsourceid><addsrcrecordid>eNo1UMlOwzAUNJtEKPmCXvIDCX7eXnyEUhapCKT2wK2ykxdkRJPKzgH-nlSUuYxmRhqNhrE58AqA25vnxe16_VYJDlBpqwxifcKuQGlErqXFU5YJibYEy9_PWG6x_s9qfs4y0IKXBpS9ZHlKn3yCEYjaZszcLV_ui26IBX3vI6UUhr4Yo-vT5O3ceJChLzrXUBGpGT76cPCu2UXnvhLlR56xzcNys3gqV6-P09hVGQD1WLa1aUSnjG-9V5prbTyXoKk12ArynMABOvJSKikstUIr1xjTcNWhBiVnbP5XG4hou49h5-LP9viA_AUajkr9</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>BEMD for expression transformation in face recognition</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Mohammadzade, Hoda ; Agrafioti, Foteini ; Jiexin Gao ; Hatzinakos, Dimitrios</creator><creatorcontrib>Mohammadzade, Hoda ; Agrafioti, Foteini ; Jiexin Gao ; Hatzinakos, Dimitrios</creatorcontrib><description>This work presents a novel methodology for the transformation of facial expressions, to assist face biometrics. It is known that identification using only one image per subject poses a great challenge to recognizers. This is because drastic facial expressions introduce variability, on which the recognizer is not trained. The proposed framework uses only one image per subject to predict intra-class variability, by synthesizing new expressions, which are subsequently used to train the discriminant. The expression of the gallery is transformed using the bivariate empirical mode decomposition (BEMD), which allows for simultaneous analysis of the probe image and a targeted expression mask. We advocate that 2D BEMD is a powerful tool for multi-resolution face analysis. The performance of the proposed framework, tested over a database of 96 individuals, is 90% for an FAR of 1%.</description><identifier>ISSN: 1520-6149</identifier><identifier>ISBN: 9781457705380</identifier><identifier>ISBN: 1457705389</identifier><identifier>EISSN: 2379-190X</identifier><identifier>EISBN: 1457705397</identifier><identifier>EISBN: 9781457705373</identifier><identifier>EISBN: 9781457705397</identifier><identifier>EISBN: 1457705370</identifier><identifier>DOI: 10.1109/ICASSP.2011.5946778</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biometrics ; correlation coefficient ; Empirical mode decomposition ; Face ; Face recognition ; Image recognition ; linear discriminant analysis ; Probes ; Video sequences</subject><ispartof>2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, p.1501-1504</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/5946778$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5946778$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Mohammadzade, Hoda</creatorcontrib><creatorcontrib>Agrafioti, Foteini</creatorcontrib><creatorcontrib>Jiexin Gao</creatorcontrib><creatorcontrib>Hatzinakos, Dimitrios</creatorcontrib><title>BEMD for expression transformation in face recognition</title><title>2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</title><addtitle>ICASSP</addtitle><description>This work presents a novel methodology for the transformation of facial expressions, to assist face biometrics. It is known that identification using only one image per subject poses a great challenge to recognizers. This is because drastic facial expressions introduce variability, on which the recognizer is not trained. The proposed framework uses only one image per subject to predict intra-class variability, by synthesizing new expressions, which are subsequently used to train the discriminant. The expression of the gallery is transformed using the bivariate empirical mode decomposition (BEMD), which allows for simultaneous analysis of the probe image and a targeted expression mask. We advocate that 2D BEMD is a powerful tool for multi-resolution face analysis. The performance of the proposed framework, tested over a database of 96 individuals, is 90% for an FAR of 1%.</description><subject>Biometrics</subject><subject>correlation coefficient</subject><subject>Empirical mode decomposition</subject><subject>Face</subject><subject>Face recognition</subject><subject>Image recognition</subject><subject>linear discriminant analysis</subject><subject>Probes</subject><subject>Video sequences</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781457705380</isbn><isbn>1457705389</isbn><isbn>1457705397</isbn><isbn>9781457705373</isbn><isbn>9781457705397</isbn><isbn>1457705370</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UMlOwzAUNJtEKPmCXvIDCX7eXnyEUhapCKT2wK2ykxdkRJPKzgH-nlSUuYxmRhqNhrE58AqA25vnxe16_VYJDlBpqwxifcKuQGlErqXFU5YJibYEy9_PWG6x_s9qfs4y0IKXBpS9ZHlKn3yCEYjaZszcLV_ui26IBX3vI6UUhr4Yo-vT5O3ceJChLzrXUBGpGT76cPCu2UXnvhLlR56xzcNys3gqV6-P09hVGQD1WLa1aUSnjG-9V5prbTyXoKk12ArynMABOvJSKikstUIr1xjTcNWhBiVnbP5XG4hou49h5-LP9viA_AUajkr9</recordid><startdate>201105</startdate><enddate>201105</enddate><creator>Mohammadzade, Hoda</creator><creator>Agrafioti, Foteini</creator><creator>Jiexin Gao</creator><creator>Hatzinakos, Dimitrios</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201105</creationdate><title>BEMD for expression transformation in face recognition</title><author>Mohammadzade, Hoda ; Agrafioti, Foteini ; Jiexin Gao ; Hatzinakos, Dimitrios</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-d86c2f46bdbb450556b0315ed67d2eb0e1a17aeb334329ed254ac66c04f75143</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Biometrics</topic><topic>correlation coefficient</topic><topic>Empirical mode decomposition</topic><topic>Face</topic><topic>Face recognition</topic><topic>Image recognition</topic><topic>linear discriminant analysis</topic><topic>Probes</topic><topic>Video sequences</topic><toplevel>online_resources</toplevel><creatorcontrib>Mohammadzade, Hoda</creatorcontrib><creatorcontrib>Agrafioti, Foteini</creatorcontrib><creatorcontrib>Jiexin Gao</creatorcontrib><creatorcontrib>Hatzinakos, Dimitrios</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>Mohammadzade, Hoda</au><au>Agrafioti, Foteini</au><au>Jiexin Gao</au><au>Hatzinakos, Dimitrios</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>BEMD for expression transformation in face recognition</atitle><btitle>2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</btitle><stitle>ICASSP</stitle><date>2011-05</date><risdate>2011</risdate><spage>1501</spage><epage>1504</epage><pages>1501-1504</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781457705380</isbn><isbn>1457705389</isbn><eisbn>1457705397</eisbn><eisbn>9781457705373</eisbn><eisbn>9781457705397</eisbn><eisbn>1457705370</eisbn><abstract>This work presents a novel methodology for the transformation of facial expressions, to assist face biometrics. It is known that identification using only one image per subject poses a great challenge to recognizers. This is because drastic facial expressions introduce variability, on which the recognizer is not trained. The proposed framework uses only one image per subject to predict intra-class variability, by synthesizing new expressions, which are subsequently used to train the discriminant. The expression of the gallery is transformed using the bivariate empirical mode decomposition (BEMD), which allows for simultaneous analysis of the probe image and a targeted expression mask. We advocate that 2D BEMD is a powerful tool for multi-resolution face analysis. The performance of the proposed framework, tested over a database of 96 individuals, is 90% for an FAR of 1%.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2011.5946778</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1520-6149 |
| ispartof | 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, p.1501-1504 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_5946778 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biometrics correlation coefficient Empirical mode decomposition Face Face recognition Image recognition linear discriminant analysis Probes Video sequences |
| title | BEMD for expression transformation in face recognition |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T21%3A43%3A47IST&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=BEMD%20for%20expression%20transformation%20in%20face%20recognition&rft.btitle=2011%20IEEE%20International%20Conference%20on%20Acoustics,%20Speech%20and%20Signal%20Processing%20(ICASSP)&rft.au=Mohammadzade,%20Hoda&rft.date=2011-05&rft.spage=1501&rft.epage=1504&rft.pages=1501-1504&rft.issn=1520-6149&rft.eissn=2379-190X&rft.isbn=9781457705380&rft.isbn_list=1457705389&rft_id=info:doi/10.1109/ICASSP.2011.5946778&rft_dat=%3Cieee_6IE%3E5946778%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1457705397&rft.eisbn_list=9781457705373&rft.eisbn_list=9781457705397&rft.eisbn_list=1457705370&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5946778&rfr_iscdi=true |