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Feature constraint reinforcement based age estimation

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Abstract

As one of the critical biological characteristics of human age, the face has been widely studied for age prediction, which has broad application prospects in the fields of commerce, security, entertainment, etc. Duo to complicated multi-latent heterogeneous features(e.g. gender) bring valuable messages for the image-based age estimation. A variety of methods utilize heterogeneous information for age estimation. However, heterogeneous features may have uncertain noise, and exploiting them without evaluating the reliability of confidence influence may impact the estimation accuracy. Inspired by the observation that gender has a noticeable impact on face at some particular age stage, this paper proposes a Feature Constraint Reinforcement Network (FCRN) to take advantage of constraint gender influence on the age estimation. The model extracts multi-scale latent heterogeneous features and deduces their confidence of influence upon age estimation methods. Specifically, it gets the gender and age features by classification and regression. Then, the model uses the gender factors extracted from the constraint gender features to reinforce and calculate the influence of different genders on age predictions among different age groups and improve the result of age prediction. Extensive experiments were conducted on the existing public aging datasets. The results show the effectiveness and superiority of the proposed method.

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Data Availability

The datasets analysed during the current study are available in the [1, 39, 46, 65], respectively. These datasets were derived from the following public domain resources: https://uncw.edu/oic/tech/morph.html, https://susanqq.github.io/UTKFace/, https://github.com/afad-dataset/tarball, https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/.

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Acknowledgements

The authors would like to thank the funding from the Open Project Program of Shanghai Key Laboratory of Data Science (No. 2020090600004), the Science and Technology Project of Jiangxi Provincial Department of Education (No.GJJ181503), (No.GJJ218513) and the resources and technical support from the High performance computing Center of Shanghai University, and Shanghai Engineering Research Center of Intelligent Computing System (No. 19DZ2252600).

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, No.333 Nanchen Rd., Shanghai, 200444, China

    Gan Chen, Junjie Peng, Lu Wang, Haochen Yuan & Yansong Huang

  2. Information Engineering, Gannan University of Science and Technology, No.156 Kejia Blvd., Ganzhou, 341000, Jiangxi, China

    Gan Chen

  3. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, No.333 Nanchen Rd., Shanghai, 200444, China

    Junjie Peng

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  1. Gan Chen
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Chen, G., Peng, J., Wang, L. et al. Feature constraint reinforcement based age estimation. Multimed Tools Appl 82, 17033–17054 (2023). https://doi.org/10.1007/s11042-022-14094-2

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