Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition
Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keyp...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mian, Ajmal S. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2007 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC 2007 |
|---|
| Übergeordnetes Werk: |
Enthalten in: International journal of computer vision - Springer US, 1987, 79(2007), 1 vom: 25. Sept., Seite 1-12 |
|---|---|
| Übergeordnetes Werk: |
volume:79 ; year:2007 ; number:1 ; day:25 ; month:09 ; pages:1-12 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11263-007-0085-5 |
|---|
| Katalog-ID: |
OLC205774311X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC205774311X | ||
| 003 | DE-627 | ||
| 005 | 20230504072059.0 | ||
| 007 | tu | ||
| 008 | 200819s2007 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11263-007-0085-5 |2 doi | |
| 035 | |a (DE-627)OLC205774311X | ||
| 035 | |a (DE-He213)s11263-007-0085-5-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Mian, Ajmal S. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition |
| 264 | 1 | |c 2007 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC 2007 | ||
| 520 | |a Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. | ||
| 650 | 4 | |a Feature-based face recognition | |
| 650 | 4 | |a Keypoint detection | |
| 650 | 4 | |a Invariant features | |
| 650 | 4 | |a Feature-level fusion | |
| 700 | 1 | |a Bennamoun, Mohammed |4 aut | |
| 700 | 1 | |a Owens, Robyn |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of computer vision |d Springer US, 1987 |g 79(2007), 1 vom: 25. Sept., Seite 1-12 |w (DE-627)129354252 |w (DE-600)155895-X |w (DE-576)018081428 |x 0920-5691 |7 nnns |
| 773 | 1 | 8 | |g volume:79 |g year:2007 |g number:1 |g day:25 |g month:09 |g pages:1-12 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11263-007-0085-5 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2012 | ||
| 912 | |a GBV_ILN_2244 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 79 |j 2007 |e 1 |b 25 |c 09 |h 1-12 | ||
| author_variant |
a s m as asm m b mb r o ro |
|---|---|
| matchkey_str |
article:09205691:2007----::epiteetoadoafaueacigotxue |
| hierarchy_sort_str |
2007 |
| publishDate |
2007 |
| allfields |
10.1007/s11263-007-0085-5 doi (DE-627)OLC205774311X (DE-He213)s11263-007-0085-5-p DE-627 ger DE-627 rakwb eng 004 VZ Mian, Ajmal S. verfasserin aut Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. Feature-based face recognition Keypoint detection Invariant features Feature-level fusion Bennamoun, Mohammed aut Owens, Robyn aut Enthalten in International journal of computer vision Springer US, 1987 79(2007), 1 vom: 25. Sept., Seite 1-12 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:79 year:2007 number:1 day:25 month:09 pages:1-12 https://doi.org/10.1007/s11263-007-0085-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 79 2007 1 25 09 1-12 |
| spelling |
10.1007/s11263-007-0085-5 doi (DE-627)OLC205774311X (DE-He213)s11263-007-0085-5-p DE-627 ger DE-627 rakwb eng 004 VZ Mian, Ajmal S. verfasserin aut Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. Feature-based face recognition Keypoint detection Invariant features Feature-level fusion Bennamoun, Mohammed aut Owens, Robyn aut Enthalten in International journal of computer vision Springer US, 1987 79(2007), 1 vom: 25. Sept., Seite 1-12 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:79 year:2007 number:1 day:25 month:09 pages:1-12 https://doi.org/10.1007/s11263-007-0085-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 79 2007 1 25 09 1-12 |
| allfields_unstemmed |
10.1007/s11263-007-0085-5 doi (DE-627)OLC205774311X (DE-He213)s11263-007-0085-5-p DE-627 ger DE-627 rakwb eng 004 VZ Mian, Ajmal S. verfasserin aut Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. Feature-based face recognition Keypoint detection Invariant features Feature-level fusion Bennamoun, Mohammed aut Owens, Robyn aut Enthalten in International journal of computer vision Springer US, 1987 79(2007), 1 vom: 25. Sept., Seite 1-12 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:79 year:2007 number:1 day:25 month:09 pages:1-12 https://doi.org/10.1007/s11263-007-0085-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 79 2007 1 25 09 1-12 |
| allfieldsGer |
10.1007/s11263-007-0085-5 doi (DE-627)OLC205774311X (DE-He213)s11263-007-0085-5-p DE-627 ger DE-627 rakwb eng 004 VZ Mian, Ajmal S. verfasserin aut Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. Feature-based face recognition Keypoint detection Invariant features Feature-level fusion Bennamoun, Mohammed aut Owens, Robyn aut Enthalten in International journal of computer vision Springer US, 1987 79(2007), 1 vom: 25. Sept., Seite 1-12 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:79 year:2007 number:1 day:25 month:09 pages:1-12 https://doi.org/10.1007/s11263-007-0085-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 79 2007 1 25 09 1-12 |
| allfieldsSound |
10.1007/s11263-007-0085-5 doi (DE-627)OLC205774311X (DE-He213)s11263-007-0085-5-p DE-627 ger DE-627 rakwb eng 004 VZ Mian, Ajmal S. verfasserin aut Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. Feature-based face recognition Keypoint detection Invariant features Feature-level fusion Bennamoun, Mohammed aut Owens, Robyn aut Enthalten in International journal of computer vision Springer US, 1987 79(2007), 1 vom: 25. Sept., Seite 1-12 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:79 year:2007 number:1 day:25 month:09 pages:1-12 https://doi.org/10.1007/s11263-007-0085-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 79 2007 1 25 09 1-12 |
| language |
English |
| source |
Enthalten in International journal of computer vision 79(2007), 1 vom: 25. Sept., Seite 1-12 volume:79 year:2007 number:1 day:25 month:09 pages:1-12 |
| sourceStr |
Enthalten in International journal of computer vision 79(2007), 1 vom: 25. Sept., Seite 1-12 volume:79 year:2007 number:1 day:25 month:09 pages:1-12 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Feature-based face recognition Keypoint detection Invariant features Feature-level fusion |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
International journal of computer vision |
| authorswithroles_txt_mv |
Mian, Ajmal S. @@aut@@ Bennamoun, Mohammed @@aut@@ Owens, Robyn @@aut@@ |
| publishDateDaySort_date |
2007-09-25T00:00:00Z |
| hierarchy_top_id |
129354252 |
| dewey-sort |
14 |
| id |
OLC205774311X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC205774311X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504072059.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2007 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11263-007-0085-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC205774311X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11263-007-0085-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mian, Ajmal S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2007</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature-based face recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Keypoint detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Invariant features</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature-level fusion</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bennamoun, Mohammed</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Owens, Robyn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of computer vision</subfield><subfield code="d">Springer US, 1987</subfield><subfield code="g">79(2007), 1 vom: 25. Sept., Seite 1-12</subfield><subfield code="w">(DE-627)129354252</subfield><subfield code="w">(DE-600)155895-X</subfield><subfield code="w">(DE-576)018081428</subfield><subfield code="x">0920-5691</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:79</subfield><subfield code="g">year:2007</subfield><subfield code="g">number:1</subfield><subfield code="g">day:25</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:1-12</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11263-007-0085-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">79</subfield><subfield code="j">2007</subfield><subfield code="e">1</subfield><subfield code="b">25</subfield><subfield code="c">09</subfield><subfield code="h">1-12</subfield></datafield></record></collection>
|
| author |
Mian, Ajmal S. |
| spellingShingle |
Mian, Ajmal S. ddc 004 misc Feature-based face recognition misc Keypoint detection misc Invariant features misc Feature-level fusion Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition |
| authorStr |
Mian, Ajmal S. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129354252 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0920-5691 |
| topic_title |
004 VZ Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition Feature-based face recognition Keypoint detection Invariant features Feature-level fusion |
| topic |
ddc 004 misc Feature-based face recognition misc Keypoint detection misc Invariant features misc Feature-level fusion |
| topic_unstemmed |
ddc 004 misc Feature-based face recognition misc Keypoint detection misc Invariant features misc Feature-level fusion |
| topic_browse |
ddc 004 misc Feature-based face recognition misc Keypoint detection misc Invariant features misc Feature-level fusion |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
International journal of computer vision |
| hierarchy_parent_id |
129354252 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
International journal of computer vision |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129354252 (DE-600)155895-X (DE-576)018081428 |
| title |
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition |
| ctrlnum |
(DE-627)OLC205774311X (DE-He213)s11263-007-0085-5-p |
| title_full |
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition |
| author_sort |
Mian, Ajmal S. |
| journal |
International journal of computer vision |
| journalStr |
International journal of computer vision |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2007 |
| contenttype_str_mv |
txt |
| container_start_page |
1 |
| author_browse |
Mian, Ajmal S. Bennamoun, Mohammed Owens, Robyn |
| container_volume |
79 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Mian, Ajmal S. |
| doi_str_mv |
10.1007/s11263-007-0085-5 |
| dewey-full |
004 |
| title_sort |
keypoint detection and local feature matching for textured 3d face recognition |
| title_auth |
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition |
| abstract |
Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. © Springer Science+Business Media, LLC 2007 |
| abstractGer |
Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. © Springer Science+Business Media, LLC 2007 |
| abstract_unstemmed |
Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set. © Springer Science+Business Media, LLC 2007 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 |
| container_issue |
1 |
| title_short |
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition |
| url |
https://doi.org/10.1007/s11263-007-0085-5 |
| remote_bool |
false |
| author2 |
Bennamoun, Mohammed Owens, Robyn |
| author2Str |
Bennamoun, Mohammed Owens, Robyn |
| ppnlink |
129354252 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11263-007-0085-5 |
| up_date |
2024-07-03T16:07:47.381Z |
| _version_ |
1803574698190045184 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC205774311X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504072059.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2007 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11263-007-0085-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC205774311X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11263-007-0085-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mian, Ajmal S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2007</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature-based face recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Keypoint detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Invariant features</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature-level fusion</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bennamoun, Mohammed</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Owens, Robyn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of computer vision</subfield><subfield code="d">Springer US, 1987</subfield><subfield code="g">79(2007), 1 vom: 25. Sept., Seite 1-12</subfield><subfield code="w">(DE-627)129354252</subfield><subfield code="w">(DE-600)155895-X</subfield><subfield code="w">(DE-576)018081428</subfield><subfield code="x">0920-5691</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:79</subfield><subfield code="g">year:2007</subfield><subfield code="g">number:1</subfield><subfield code="g">day:25</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:1-12</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11263-007-0085-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">79</subfield><subfield code="j">2007</subfield><subfield code="e">1</subfield><subfield code="b">25</subfield><subfield code="c">09</subfield><subfield code="h">1-12</subfield></datafield></record></collection>
|
| score |
7.4014244 |