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Facial age estimation by nonlinear aging pattern subspace

Geng, Xin, Smith-Miles, Kate and Zhou, Zhi-Hua 2008, Facial age estimation by nonlinear aging pattern subspace, in MM 2008 : Proceedings of the 2008 ACM International Conference on Multimedia, with co-located symposium & workshops : Vancouver, BC, Canada, October 27-31, 2008 : AREA '08, CommunicabilityMS '08, HCC '08, MIR '08, MS '08, SAME '08, SRMC '08, TVS '08, VNBA '08, Association for Computing Machinery, [New York, N.Y.], pp. 721-724.

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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Title Facial age estimation by nonlinear aging pattern subspace
Author(s) Geng, Xin
Smith-Miles, Kate
Zhou, Zhi-Hua
Conference name ACM International Conference on Multimedia (16th : 2008 : Vancouver, BC, Canada)
Conference location Vancouver, Canada
Conference dates 26-31 October 2008
Title of proceedings MM 2008 : Proceedings of the 2008 ACM International Conference on Multimedia, with co-located symposium & workshops : Vancouver, BC, Canada, October 27-31, 2008 : AREA '08, CommunicabilityMS '08, HCC '08, MIR '08, MS '08, SAME '08, SRMC '08, TVS '08, VNBA '08
Editor(s) [Unknown]
Publication date 2008
Conference series Association for Computing Machinery International Conference on Multimedia
Start page 721
End page 724
Publisher Association for Computing Machinery
Place of publication [New York, N.Y.]
Keyword(s) algorithms
Summary Human age estimation by face images is an interesting yet challenging research topic emerging in recent years. This paper extends our previous work on facial age estimation (a linear method named AGES). In order to match the nonlinear nature of the human aging progress, a new algorithm named KAGES is proposed based on a nonlinear subspace trained on the aging patterns, which are defined as sequences of individual face images sorted in time order. Both the training and test (age estimation) processes of KAGES rely on a probabilistic model of KPCA. In the experimental results, the performance of KAGES is not only better than all the compared algorithms, but also better than the human observers in age estimation. The results are sensitive to parameter choice however, and future research challenges are identified.
ISBN 9781605583037
1605583030
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018154
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