Lighting Condition Adaptation for Perceived Age Estimation

Over the recent years, a great deal of effort has been made to age estimation 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 real-world environment because of 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 realworld age estimation experiments, we demonstrate the usefulness of our proposed method. Keywords face recognition, age estimation, covariate shift adaptation, lighting condition change, Kullback-Leibler importance estimation procedure, importance-weighted regularized least-squares

• Type: Journal
• Year: 2011
• Where: IEICET
• Authors: Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara
• Keywords: Age Estimation, Covariate Shift, Estimation, IEICET 2011, Information Technology