liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation
Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia.ORCID iD: 0000-0002-5861-7076
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6763-5487
Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia.
2019 (English)In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2019), SciTePress, 2019, Vol. 5, p. 209-217Conference paper, Published paper (Refereed)
Abstract [en]

Availability of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, datasets with age and gender annotations are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts weakly labeled noisy data. The unsupervised facial biometric data filtering method presented in this paper greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped facial datasets demonstrate the effectiveness of the proposed method, with respect to training and validation scores, training convergence, and generalization capabilities of trained age and gender estimators.

Place, publisher, year, edition, pages
SciTePress, 2019. Vol. 5, p. 209-217
Keywords [en]
Biometric; Web-Scraping; Age; Gender
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-154867DOI: 10.5220/0007257202090217ISI: 000570349800021ISBN: 978-989-758-354-4 (electronic)OAI: oai:DiVA.org:liu-154867DiVA, id: diva2:1292960
Conference
14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), Prague, CZECH REPUBLIC, feb 25-27, 2019
Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2022-05-04Bibliographically approved

Open Access in DiVA

fulltext(3661 kB)252 downloads
File information
File name FULLTEXT01.pdfFile size 3661 kBChecksum SHA-512
4939178171b5492346dc24d616ab2eb463e3319ddb272c06838aa567467270a20e8a8cd14cb21c82fc6515a3cebe190d2f6e8a3c049d3bcebe3d002b99c6a725
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Ahlberg, Jörgen

Search in DiVA

By author/editor
Bešenić, KrešimirAhlberg, Jörgen
By organisation
Computer VisionFaculty of Science & Engineering
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 253 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 1114 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf