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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RN5HUS
Repositorysid.inpe.br/sibgrapi/2018/08.27.14.28
Last Update2018:08.27.14.28.52 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.27.14.28.52
Metadata Last Update2022:06.14.00.09.08 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00022
Citation KeyJung:2018:ReGeDe
TitleReal-Time Gender Detection in the Wild Using Deep Neural Networks
FormatOn-line
Year2018
Access Date2024, July 22
Number of Files1
Size3391 KiB
2. Context
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
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-08-27 14:28:52 :: luis.zeni@inf.ufrgs.br -> administrator ::
2022-06-14 00:09:08 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Real-Time Gender Detection...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Real-Time Gender Detection...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RN5HUS
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RN5HUS
Languageen
Target File87.pdf
User Groupluis.zeni@inf.ufrgs.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 17
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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