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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3RN5HUS
Repositorysid.inpe.br/sibgrapi/2018/08.27.14.28
Last Update2018:08.27.14.28.52 administrator
Metadatasid.inpe.br/sibgrapi/2018/08.27.14.28.52
Metadata Last Update2020:02.19.03.10.44 administrator
Citation KeyJung:2018:ReGeDe
TitleReal-Time Gender Detection in the Wild Using Deep Neural Networks
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size3391 KiB
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Author1 zeni, luis felipe de araujo
2 Jung, Claudio Rosito
Affiliation1 universidade federal do rio grande do sul
2 universidade federal do rio grande do sul
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 Addressluis.zeni@inf.ufrgs.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-08-27 14:28:52 :: luis.zeni@inf.ufrgs.br -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
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Document Stagecompleted
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Transferable1
Content TypeExternal Contribution
Tertiary TypeFull Paper
Keywordsdeep learning, computer vision, gender detection, real-time.
AbstractGender recognition can be used in many applications, such as video surveillance, human-computer interaction and customized advertisement. Current state-of-the-art gender recognition methods are detector-dependent or region-dependent, focusing mostly on facial features (a face detector is typically required). These limitations do not allow an end-to-end training pipeline, and many features used in the detection phase must be re-learned in the classification step. Furthermore, the use of facial features limits the application of such methods in the wild, where the face might not be present. This paper presents a real-time end-to-end gender detector based on deep neural networks. The proposed method detects and recognizes the gender of persons in the wild, meaning in images with a high variability in pose, illumination an occlusions. To train and evaluate the results a new annotation set of Pascal VOC 2007 and CelebA were created. Our experimental results indicate that combining both datasets during training can increase the mAp of our gender detector. We also visually analyze which parts leads our network to make mistakes and the bias introduced by the training data.
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User Groupluis.zeni@inf.ufrgs.br
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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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