Lighting Condition Adaptation for Perceived Age Estimation
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.2 pp.392-395
- Publication Date
- 2011/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.392
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
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Keyword
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Kazuya UEKI, Masashi SUGIYAMA, Yasuyuki IHARA, "Lighting Condition Adaptation for Perceived Age Estimation" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 2, pp. 392-395, February 2011, doi: 10.1587/transinf.E94.D.392.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.392/_p
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@ARTICLE{e94-d_2_392,
author={Kazuya UEKI, Masashi SUGIYAMA, Yasuyuki IHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Lighting Condition Adaptation for Perceived Age Estimation},
year={2011},
volume={E94-D},
number={2},
pages={392-395},
abstract={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.},
keywords={},
doi={10.1587/transinf.E94.D.392},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Lighting Condition Adaptation for Perceived Age Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 392
EP - 395
AU - Kazuya UEKI
AU - Masashi SUGIYAMA
AU - Yasuyuki IHARA
PY - 2011
DO - 10.1587/transinf.E94.D.392
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2011
AB - 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.
ER -
English
