Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method
Resumen:
Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features, while bypassing the complicated task of 3D reconstruction. The key idea is to project over the test face a high spatial frequency pattern, which allows us to simultaneously recover real 3D information plus a standard 2D facial image. Therefore, state-of-the-art 2D face recognition solution can be transparently applied, while from the high frequency component of the input image, complementary 3D facial features are extracted. Experimental results on ND-2006 dataset show that the proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks.
| 2020 | |
|
Differential 3D Active stereo Face recognition Spoofing detection 3D facial analysis Three-dimensional displays Two dimensional displays Feature extraction Facial features Image resolution Data mining |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/52230 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución (CC - By 4.0) |
| _version_ | 1872864820503511040 |
|---|---|
| author | Di Martino, Matías |
| author2 | Suzacq, Fernando Delbracio, Mauricio Qiu, Qiang Sapiro, Guillermo |
| author2_role | author author author author |
| author_facet | Di Martino, Matías Suzacq, Fernando Delbracio, Mauricio Qiu, Qiang Sapiro, Guillermo |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Di Martino Matías, Universidad de la República (Uruguay). Facultad de Ingeniería. Suzacq Fernando, Universidad de la República (Uruguay). Facultad de Ingeniería. Delbracio Mauricio, Universidad de la República (Uruguay). Facultad de Ingeniería. Qiu Qiang, Duke University, Durham, USA Sapiro Guillermo, Duke University, Durham, USA |
| dc.creator.none.fl_str_mv | Di Martino, Matías Suzacq, Fernando Delbracio, Mauricio Qiu, Qiang Sapiro, Guillermo |
| dc.date.accessioned.none.fl_str_mv | 2025-10-24T17:37:55Z |
| dc.date.available.none.fl_str_mv | 2025-10-24T17:37:55Z |
| dc.date.issued.none.fl_str_mv | 2020 |
| dc.description.abstract.none.fl_txt_mv | Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features, while bypassing the complicated task of 3D reconstruction. The key idea is to project over the test face a high spatial frequency pattern, which allows us to simultaneously recover real 3D information plus a standard 2D facial image. Therefore, state-of-the-art 2D face recognition solution can be transparently applied, while from the high frequency component of the input image, complementary 3D facial features are extracted. Experimental results on ND-2006 dataset show that the proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks. |
| dc.format.extent.es.fl_str_mv | 36 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Di Martino, M., Suzacq, F., Delbracio, M. y otros. Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method [Preprint]. Publicado en : IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no 7, jul. 2020, pp. 1582-1593. DOI: 10.1109/TPAMI.2020.2986951. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/52230 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Differential 3D Active stereo Face recognition Spoofing detection 3D facial analysis Three-dimensional displays Two dimensional displays Feature extraction Facial features Image resolution Data mining |
| dc.title.none.fl_str_mv | Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features, while bypassing the complicated task of 3D reconstruction. The key idea is to project over the test face a high spatial frequency pattern, which allows us to simultaneously recover real 3D information plus a standard 2D facial image. Therefore, state-of-the-art 2D face recognition solution can be transparently applied, while from the high frequency component of the input image, complementary 3D facial features are extracted. Experimental results on ND-2006 dataset show that the proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_3f12fc75f62064506fd61d017f55da74 |
| identifier_str_mv | Di Martino, M., Suzacq, F., Delbracio, M. y otros. Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method [Preprint]. Publicado en : IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no 7, jul. 2020, pp. 1582-1593. DOI: 10.1109/TPAMI.2020.2986951. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/52230 |
| publishDate | 2020 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | karina.camps@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Di Martino Matías, Universidad de la República (Uruguay). Facultad de Ingeniería.Suzacq Fernando, Universidad de la República (Uruguay). Facultad de Ingeniería.Delbracio Mauricio, Universidad de la República (Uruguay). Facultad de Ingeniería.Qiu Qiang, Duke University, Durham, USASapiro Guillermo, Duke University, Durham, USA2025-10-24T17:37:55Z2025-10-24T17:37:55Z2020Di Martino, M., Suzacq, F., Delbracio, M. y otros. Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method [Preprint]. Publicado en : IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no 7, jul. 2020, pp. 1582-1593. DOI: 10.1109/TPAMI.2020.2986951.https://hdl.handle.net/20.500.12008/52230Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features, while bypassing the complicated task of 3D reconstruction. The key idea is to project over the test face a high spatial frequency pattern, which allows us to simultaneously recover real 3D information plus a standard 2D facial image. Therefore, state-of-the-art 2D face recognition solution can be transparently applied, while from the high frequency component of the input image, complementary 3D facial features are extracted. Experimental results on ND-2006 dataset show that the proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2025-10-22T17:17:47Z No. of bitstreams: 2 license_rdf: 24942 bytes, checksum: 58cb336ce230a47d2f88ad02838a665f (MD5) DSDQS20.pdf: 2274282 bytes, checksum: 8a5d40f6cf0cc2a5835927581a0a995e (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2025-10-24T17:12:26Z (GMT) No. of bitstreams: 2 license_rdf: 24942 bytes, checksum: 58cb336ce230a47d2f88ad02838a665f (MD5) DSDQS20.pdf: 2274282 bytes, checksum: 8a5d40f6cf0cc2a5835927581a0a995e (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2025-10-24T17:37:55Z (GMT). No. of bitstreams: 2 license_rdf: 24942 bytes, checksum: 58cb336ce230a47d2f88ad02838a665f (MD5) DSDQS20.pdf: 2274282 bytes, checksum: 8a5d40f6cf0cc2a5835927581a0a995e (MD5) Previous issue date: 202036 p.application/pdfenengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)Differential 3DActive stereoFace recognitionSpoofing detection3D facial analysisThree-dimensional displaysTwo dimensional displaysFeature extractionFacial featuresImage resolutionData miningDifferential 3D facial recognition : Adding 3D to your state-of-the-art 2D methodPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaDi Martino, MatíasSuzacq, FernandoDelbracio, MauricioQiu, QiangSapiro, GuillermoLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/52230/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-844http://localhost:8080/xmlui/bitstream/20.500.12008/52230/2/license_urla0ebbeafb9d2ec7cbb19d7137ebc392cMD52license_textlicense_texttext/html; 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públicahttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestkarina.camps@seciu.edu.uyUruguayopendoar:47712025-10-24T17:37:55COLIBRI - Universidad de la Repúblicafalse |
| spellingShingle | Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method Di Martino, Matías Differential 3D Active stereo Face recognition Spoofing detection 3D facial analysis Three-dimensional displays Two dimensional displays Feature extraction Facial features Image resolution Data mining |
| status_str | submittedVersion |
| title | Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method |
| title_full | Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method |
| title_fullStr | Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method |
| title_full_unstemmed | Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method |
| title_short | Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method |
| title_sort | Differential 3D facial recognition : Adding 3D to your state-of-the-art 2D method |
| topic | Differential 3D Active stereo Face recognition Spoofing detection 3D facial analysis Three-dimensional displays Two dimensional displays Feature extraction Facial features Image resolution Data mining |
| url | https://hdl.handle.net/20.500.12008/52230 |
