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Automatic age estimation based on facial aging patterns

Geng, Xin, Zhou, Zhi-Hua and Smith-Miles, Kate 2007, Automatic age estimation based on facial aging patterns, IEEE transactions on pattern analysis and machine intelligence, vol. 29, no. 12, pp. 2234-2240.

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Title Automatic age estimation based on facial aging patterns
Author(s) Geng, Xin
Zhou, Zhi-Hua
Smith-Miles, Kate
Journal name IEEE transactions on pattern analysis and machine intelligence
Volume number 29
Issue number 12
Start page 2234
End page 2240
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2007-12
ISSN 0162-8828
Keyword(s) computer vision
age estimation
face and gesture recognition
machine learning
pattern recognition
Summary While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language eng
Field of Research 080104 Computer Vision
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007652

Document type: Journal Article
Collections: School of Engineering and Information Technology
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Created: Mon, 29 Sep 2008, 08:54:34 EST