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Relationships Self-Learning Based Gender-Aware Age Estimation

  • Qing Tian
  • Meng Cao
  • Songcan ChenEmail author
  • Hujun Yin
Article
  • 12 Downloads

Abstract

In biometrics research, face-appearance based age estimation (AE) becomes an important topic and has attracted a great deal of attention due to its potential applications. To achieve the goal of AE, a variety of methods have been proposed in the literature, among which the cumulative attribute (CA) coding based methods have achieved promising performance by preserving both ordinality and neighbor-similarity of ages. However, the sub-regressors responsible for regressing each of the CA coding elements are learned separately, while all of them are trained on the same dataset, leading to that potential correlation relationships of inter/intra-CA coding are not exploited. To this end, we herein propose a novel correlation learning method to model and utilize such inter/intra-CA relationships for AE, through self-learning from the training data. In addition, we extend the proposed method to perform gender-aware AE by further exploiting the correlations between and within gender groups. Furthermore, we introduce an alternating optimization algorithm for the proposed methods. Extensive experiments are conducted to demonstrate that the proposed methods can significantly improve the accuracy of AE, and more importantly that they can model well both inter/intra CA coding and gender relationships, regardless whether they are related (positive or negative) or not.

Keywords

Age estimation Cumulative attribute Correlation learning strategy Gender-aware age estimation 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grants 61702273 and 61472186, the Natural Science Foundation of Jiangsu Province under Grant BK20170956, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 17KJB520022, the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Startup Foundation for Talents of Nanjing University of Information Science and Technology.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Qing Tian
    • 1
    • 2
    • 4
  • Meng Cao
    • 1
    • 2
  • Songcan Chen
    • 3
    Email author
  • Hujun Yin
    • 4
  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  2. 2.Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  3. 3.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  4. 4.School of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK

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