Lighting condition adaptation for perceived age estimation

Kazuya Ueki*, Masashi Sugiyama, Yasuyuki Ihara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Over the recent years, a great deal of effort has been made to estimate age from face images. It has been reported that age can be accurately estimated under controlled environment such as frontal faces, no expression, and static lighting conditions. However, it is not straightforward to achieve the same accuracy level in a real-world environment due to considerable variations in camera settings, facial poses, and illumination conditions. In this paper, we apply a recently proposed machine learning technique called covariate shift adaptation to alleviating lighting condition change between laboratory and practical environment. Through real-world age estimation experiments, we demonstrate the usefulness of our proposed method.

Original languageEnglish
Pages (from-to)392-395
Number of pages4
JournalIEICE Transactions on Information and Systems
Issue number2
Publication statusPublished - 2011 Feb
Externally publishedYes


  • Age estimation
  • Covariate shift adaptation
  • Face recognition
  • Importance-weighted regularized least-squares
  • Kullback-Leibler importance estimation procedure
  • Lighting condition change

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Software
  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition


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