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

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Two-Stream Part-based Deep Representation for Human Attribute Recognition
Aalto Univ, Finland.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Aalto Univ, Finland.
2018 (English)In: 2018 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB), IEEE , 2018, p. 90-97Conference paper, Published paper (Refereed)
Abstract [en]

Recognizing human attributes in unconstrained environments is a challenging computer vision problem. State-of-the-art approaches to human attribute recognition are based on convolutional neural networks (CNNs). The de facto practice when training these CNNs on a large labeled image dataset is to take RGB pixel values of an image as input to the network. In this work, we propose a two-stream part-based deep representation for human attribute classification. Besides the standard RGB stream, we train a deep network by using mapped coded images with explicit texture information, that complements the standard RGB deep model. To integrate human body parts knowledge, we employ the deformable part-based models together with our two-stream deep model. Experiments are performed on the challenging Human Attributes (HAT-27) Dataset consisting of 27 different human attributes. Our results clearly show that (a) the two-stream deep network provides consistent gain in performance over the standard RGB model and (b) that the attribute classification results are further improved with our two-stream part-based deep representations, leading to state-of-the-art results.

Place, publisher, year, edition, pages
IEEE , 2018. p. 90-97
Series
International Conference on Biometrics, ISSN 2376-4201
Keywords [en]
Deep learning; Human attribute recognition; Part-based representation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-152842DOI: 10.1109/ICB2018.2018.00024ISI: 000449428100013ISBN: 978-1-5386-4285-6 (print)OAI: oai:DiVA.org:liu-152842DiVA, id: diva2:1265191
Conference
11th IAPR International Conference on Biometrics (ICB)
Note

Funding Agencies|Academy of Finland [251170]; H2020-ICT project MeMAD [780069]; VR starting grant [2016-05543]; Nvidia; SSF

Available from: 2018-11-22 Created: 2018-11-22 Last updated: 2018-11-22

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Khan, Fahad
By organisation
Computer VisionFaculty of Science & Engineering
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 159 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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