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2014 | OriginalPaper | Buchkapitel

8. Facial Age Estimation: A Data Representation Perspective

verfasst von : Xin Geng

Erschienen in: Human-Centered Social Media Analytics

Verlag: Springer International Publishing

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Abstract

Facial age estimation has many potential applications in the area of human-centered computing, since age plays a very important role in human society. Traditional approaches to automatic facial age estimation mainly aim to model the mapping from the face image \(\mathbf x \) to the age y. On the contrary, this chapter presents two typical solutions based on special data representation forms other than the traditional \(x\rightarrow y\) mapping, which are specially designed to match the characteristics of human facial aging effects. The first solution is called AGES (AGing pattErn Subspace), which mainly manipulates the left side of the mapping. 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 second solution is based on a new learning paradigm named label distribution learning, which mainly manipulates the right side of the mapping. The basic idea is to regard each face image as an instance associated with a label distribution which covers a certain number of age labels.

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Vorheriges Kapitel Female Facial Beauty Attribute Recognition and Editing

Nächstes Kapitel Identity and Kinship Relations in Group Pictures

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Titel: Facial Age Estimation: A Data Representation Perspective
verfasst von: Xin Geng
Verlag: Springer International Publishing
Buch: Human-Centered Social Media Analytics
Print ISBN: 978-3-319-05490-2

Electronic ISBN: 978-3-319-05491-9

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-319-05491-9_8

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