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Facial Age Estimation: A Data Representation Perspective

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Human-Centered Social Media Analytics

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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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Acknowledgments

This work was supported by the National Science Foundation of China (61273300, 61232007), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Excellent Young Teachers Program of SEU, and the Key Lab of Computer Network and Information Integration of Ministry of Education of China.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, 2, Southeast University Road, Nanjing, 211189, Jiangsu, China

    Xin Geng

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  1. Xin Geng
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Correspondence to Xin Geng .

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Editors and Affiliations

  1. Dept. of ECE, College of Engineering, Northeastern University, Boston, Massachusetts, USA

    Yun Fu

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Geng, X. (2014). Facial Age Estimation: A Data Representation Perspective. In: Fu, Y. (eds) Human-Centered Social Media Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-05491-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-05491-9_8

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  • Online ISBN: 978-3-319-05491-9

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