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Learning from facial aging patterns for automatic age estimation

Geng, Xin, Zhou, Zhi-Hua, Zhang, Yu, Li, Gang, Dai, Honghua·2006·Proceedings of the 14th ACM international conference on Multimedia
DOI 10.1145/1180639.1180711
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Abstract

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 Subspace) 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.

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Metadata

Authors
Geng, X., Zhou, Z.-H., Zhang, Y., Li, G., & Dai, H.
Affiliations
  • Deakin University, Victoria, Australia
  • Nanjing University, Nanjing, China
Venue
Proceedings of the 14th ACM international conference on Multimedia
Publisher
ACM
Published
2006-10-23
Pages
307-316
DOI
10.1145/1180639.1180711
Cited by
207
References
1

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