Lighting condition adaptation for perceived age estimation

Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara

研究成果: Article

3 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)392-395
ページ数4
ジャーナルIEICE Transactions on Information and Systems
E94-D
2
DOI
出版ステータスPublished - 2011 2
外部発表はい

ASJC Scopus subject areas

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

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