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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3S39KCL
Repositorysid.inpe.br/sibgrapi/2018/10.16.17.36
Last Update2018:10.16.17.36.59 gustavowl@lcc.ufrn.br
Metadatasid.inpe.br/sibgrapi/2018/10.16.17.36.59
Metadata Last Update2020:02.20.22.06.49 administrator
Citation KeyBezerraGome:2018:ReOcLa
TitleRecognition of occluded and lateral faces using MTCNN, Dlib and homographies
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size1307 KiB
Context area
Author1 Bezerra, Gustavo Alves
2 Gomes, Rafael Beserra
Affiliation1 Universidade Federal do Rio Grande do Norte
2 Universidade Federal do Rio Grande do Norte
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressgustavowl@lcc.ufrn.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-16 17:36:59 :: gustavowl@lcc.ufrn.br -> administrator ::
2020-02-20 22:06:49 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Transferable1
Tertiary TypeUndergraduate Work
Keywordsface recognition, occlusion, homography.
AbstractWith the advance of technology it is possible to create more robust security systems. For this task, image processing alongside Deep Neural Networks are currently being used in several works for facial recognition. However, occlusions and faces in different angles are a challenge for most algorithms. Attempting to contour this issue, an algorithm for facial recognition combining MTCNN, DLIB and homographies is proposed. In the obtained results, a comparison between the proposed algorithm and basis works indicates that, for some controlled cases, a mean accuracy improvement of 7.4% was obtained, with a maximum of 8.23% for occluded faces and 14.08% for lateral faces.
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Languageen
Target FileRecognition_of_Occluded_and_Lateral_Faces.pdf
User Groupgustavowl@lcc.ufrn.br
Visibilityshown
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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