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Automatic age estimation via sparse representation
LIANG Yixiong 1,LIU Lingbo 2 *
1.School of Information Science and Engineering, Central South University, ChangSha 410083
2.School of Information Science and Engineering, Central South University, ChangSha 410083
*Correspondence author
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Funding: Open Project Program of the State Key Lab of CAD&CG （No.0911 and A1011）, National Natural Science Funds of China（No.60803024,60970098and60903136）, Fundamental Research Funds for the Central Universities（No.201021200062）, Specialized Research Fund for the DoctoralProgram of Higher Education （No.200805331107 and 20090162110055）
Opened online:22 February 2012
Accepted by: none
Citation: LIANG Yixiong,LIU Lingbo.Automatic age estimation via sparse representation[OL]. [22 February 2012] http://en.paper.edu.cn/en_releasepaper/content/4464426
 
 
Automatic age estimation from face has received increasing attention due to its wide range of application. A successful age estimator typically consists of two key modules: age-related feature extraction and age estimation by regression or classification. In this paper we propose a novel age estimator method based on sparse representation. In the feature exaction stage, the mid-level Spatial-Pyramid face representation based on Sparse codes of SIFT features (ScSPM) is used to characterize the age-related variance. For age estimation, linear sparse regression models are learned which can not only select the most discriminative features but also perform the age estimation. The hierarchical strategy, which first coarsely classifies the faces into age groups and then finely estimates the detailed age by the linear regression model of this group, is adopted to deal with the non-linearity attribute of aging to improve the performance of the age regression model. To our best knowledge, it is the first time to apply ScSPM and sparse linear regression to age estimation. The experimental results show that the proposed approach outperforms the state-of-the-art on the FG-NET database and achieves competitive performance on the MORPH database.
Keywords:Pattern Recognition; Age Estimation; Sparse Representation; Spatial Pyramid Matching; elastic net
 
 
 

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