Data-Dependent Label Distribution Learning for Age Estimation

Zhouzhou He, Xi Li, Zhongfei Zhang, Fei Wu, Xin Geng, Yaqing Zhang, Ming Hsuan Yang, Yueting Zhuang

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with respect to several ordinal age labels, which have intrinsically cross-age correlations across adjacent age dimensions. As a result, such correlations usually lead to the age label ambiguities of the face samples. Namely, each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. Motivated by this observation, we propose a totally data-driven label distribution learning approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the basis of the local context structures of face samples. Without any prior assumptions on the forms of label distribution learning, our approach is able to flexibly model the sample-specific context aware label distribution properties by solving a multi-task problem, which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental results demonstrate the effectiveness of our approach.

Original languageEnglish
Article number7822912
Pages (from-to)3846-3858
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number8
Publication statusPublished - 2017 Aug

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant U1509206 and Grant 61472353 and in part by the Alibaba-Zhejiang University Joint Institute of Frontier Technologies.

Publisher Copyright:
© 1992-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


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