Facial age estimation by nonlinear aging pattern subspace

Xin Geng, Kate Amanda Smith-Miles, Zhi-Hua Zhou

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

46 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationInternational Multimedia Conference
EditorsAbdulmotaleb E L Saddik, Son Vuong
Place of PublicationNew York USA
PublisherAssociation for Computing Machinery (ACM)
Pages721 - 724
Number of pages4
ISBN (Print)978-1-60558-303-7
Publication statusPublished - 2008
Externally publishedYes
EventACM International Conference on Multimedia 2008 - Vancouver, Canada
Duration: 27 Oct 200831 Oct 2008
Conference number: 16th


ConferenceACM International Conference on Multimedia 2008
Abbreviated titleMM 2008
Internet address

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