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Learning from facial aging patterns for automatic age estimation
conference contribution
posted on 2006-01-01, 00:00 authored by X Geng, Z H Zhou, Y Zhang, Gang LiGang Li, Honghua DaiAge 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.
History
Event
ACM International Conference on Multimedia (14th : 2006 : Santa Barbara, Calif.)Pagination
307 - 316Publisher
Association for Computing MachineryLocation
Santa Barbara, Calif.Place of publication
New York, N.Y.Start date
2006-10-23End date
2006-10-27ISBN-13
9781595934475ISBN-10
1595934472Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2006, ACMEditor/Contributor(s)
K Nahrstedt, M TurkTitle of proceedings
ACM 2006 : multimedia & co-located workshopsUsage metrics
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