Adversarial spatial frequency domain critic learning for age and gender classification

Cited 3 time in webofscience Cited 0 time in scopus
  • Hit : 627
  • Download : 0
This paper proposes a novel deep learning framework for age and gender classification with the adversarial spatial frequency domain critic. In the proposed framework, the encoder-generator synthesizes realistic facial images with real images and corresponding age and gender label. An adversarial critic is devised to make generated images more proper for age and gender classification. In particular, we analyze the characteristic of age and gender attributes in the spatial frequency domain. Based on our investigation, we devise the spatial frequency domain critic network for considering the specific frequency bands which are dominant on age and gender attributes. Our discriminator is designed to simultaneously perform age and gender classification tasks. For this purpose, alternating learning is performed for multi-task classification. Experimental results showed that the proposed method outperformed other state-of-the-art methods in age and gender classifications.
Publisher
IEEE Signal Processing Society
Issue Date
2018-10-07
Language
English
Citation

25th IEEE International Conference on Image Processing (ICIP) 2018, pp.2032 - 2036

DOI
10.1109/ICIP.2018.8451616
URI
http://hdl.handle.net/10203/244105
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 3 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0