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Learning from facial aging patterns for automatic age estimation

Geng, Xin, Zhou, Zhi-Hua, Zhang, Yu, Li, Gang and Dai, Honghua 2006, Learning from facial aging patterns for automatic age estimation, in ACM 2006 : multimedia & co-located workshops, Association for Computing Machinery, New York, N.Y., pp. 307-316.

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Title Learning from facial aging patterns for automatic age estimation
Author(s) Geng, Xin
Zhou, Zhi-Hua
Zhang, Yu
Li, GangORCID iD for Li, Gang
Dai, Honghua
Conference name ACM International Conference on Multimedia (14th : 2006 : Santa Barbara, Calif.)
Conference location Santa Barbara, Calif.
Conference dates 23-27 Oct. 2006
Title of proceedings ACM 2006 : multimedia & co-located workshops
Editor(s) Nahrstedt, Klara
Turk, Matthew
Publication date 2006
Conference series Association for Computing Machinery International Conference on Multimedia
Start page 307
End page 316
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Summary Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Sub-space) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.
ISBN 1595934472
Language eng
Field of Research 080104 Computer Vision
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2006, ACM
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Document type: Conference Paper
Collection: School of Engineering and Information Technology
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