Perceived age estimation under lighting condition change by covariate shift adaptation

Kazuya Ueki; Masashi Sugiyama; Yasuyuki Ihara

doi:10.1109/ICPR.2010.830

Perceived age estimation under lighting condition change by covariate shift adaptation

Kazuya Ueki^*, Masashi Sugiyama, Yasuyuki Ihara

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

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 real-world age estimation experiments, we demonstrate the usefulness of our proposed method.

Original language	English
Title of host publication	Proceedings - International Conference on Pattern Recognition
Pages	3400-3403
Number of pages	4
DOIs	https://doi.org/10.1109/ICPR.2010.830
Publication status	Published - 2010
Externally published	Yes
Event	2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey Duration: 2010 Aug 23 → 2010 Aug 26

Other

Other	2010 20th International Conference on Pattern Recognition, ICPR 2010
Country/Territory	Turkey
City	Istanbul
Period	10/8/23 → 10/8/26

Keywords

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

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

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10.1109/ICPR.2010.830

Cite this

@inproceedings{e1f60e88e7f4443c85fb384af04d2750,

title = "Perceived age estimation under lighting condition change by covariate shift adaptation",

abstract = "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 real-world age estimation experiments, we demonstrate the usefulness of our proposed method.",

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

author = "Kazuya Ueki and Masashi Sugiyama and Yasuyuki Ihara",

year = "2010",

doi = "10.1109/ICPR.2010.830",

language = "English",

isbn = "9780769541099",

pages = "3400--3403",

booktitle = "Proceedings - International Conference on Pattern Recognition",

note = "2010 20th International Conference on Pattern Recognition, ICPR 2010 ; Conference date: 23-08-2010 Through 26-08-2010",

}

TY - GEN

T1 - Perceived age estimation under lighting condition change by covariate shift adaptation

AU - Ueki, Kazuya

AU - Sugiyama, Masashi

AU - Ihara, Yasuyuki

PY - 2010

Y1 - 2010

N2 - 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 real-world age estimation experiments, we demonstrate the usefulness of our proposed method.

AB - 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 real-world age estimation experiments, we demonstrate the usefulness of our proposed method.

KW - Age estimation

KW - Covariate shift adaptation

KW - Face recognition

KW - Importance-weighted regularized least-squares

KW - Kullback-Leibler importance estimation procedure

KW - Lighting condition change

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U2 - 10.1109/ICPR.2010.830

DO - 10.1109/ICPR.2010.830

M3 - Conference contribution

AN - SCOPUS:78149480678

SN - 9780769541099

SP - 3400

EP - 3403

BT - Proceedings - International Conference on Pattern Recognition

T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010

Y2 - 23 August 2010 through 26 August 2010

ER -

Perceived age estimation under lighting condition change by covariate shift adaptation

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Keywords

ASJC Scopus subject areas

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