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Real-time human cross-race aging-related face appearance detection with deep convolution architecture

  • Qing TianEmail author
  • Wenqiang Zhang
  • Junxiang Mao
  • Hujun Yin
Special Issue Paper
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Abstract

Human age estimation (AE) is an emerging research topic in computer vision and machine learning and has attracted increasing amount of research due its wide potential applications. In the process of human aging, facial appearances change from glabrous to crinkly similarly across all races, from European, Hispanic and African to Asian. To specially explore the relationships between aging and facial appearances across races, this paper is devoted to determining the correspondence between facial aging and facial appearances. Specifically, we first extract appearance vector features from facial images with their spatial structure preserved. Then, we propose to select the aging-related features shared by different races to explore their aging-related common facial regions, while removing redundant features. Thirdly, we improve the proposed model by incorporating potential cross-race relationships in an automated learning manner. Additionally, we extend our model with deep convolution architecture. Finally, we evaluate the proposed methodologies on a large face aging database with real-time efficiency.

Keywords

Human age estimation Cross-race relationships Aging-related facial appearance features Deep convolutional neural networks 

Notes

Acknowledgements

This work was supported partially by the National Natural Science Foundation of China under Grant 61702273, 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 Fundamental Research Funds for the Central Universities no. NJ2019010, the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund, and the Startup Foundation for Talents of Nanjing University of Information Science and Technology.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Qing Tian
    • 1
    • 2
    • 3
    • 4
    Email author
  • Wenqiang Zhang
    • 1
  • Junxiang Mao
    • 1
  • Hujun Yin
    • 5
  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and TechnologyNanjingChina
  4. 4.MIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing University of Aeronautics and AstronauticsNanjingChina
  5. 5.School of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK

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