CNN-Based Age Classification via Transfer Learning

  • Jian Lin
  • Tianyue Zheng
  • Yanbing Liao
  • Weihong Deng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10559)


Age estimation has always hit people’s eyes. While most previous works have focused on constrained images taken under lab condition, which is far from real-world age estimation. The benchmark we used in this paper is the unconstrained Adience [3], which is believed to better reflect the traits of age in the wild condition. In this paper, we adapted contrastive loss to fine-tune the pre-trained VGG-16 over FG-NET to get a better start point and proposed AvgOut-FC Layer to enhance the performance of the models over Audience. We have achieved better results over the Adience benchmark than previous works, which demonstrated the effectiveness of our methods.


Unconstrained age estimation Convolutional Neural Network Transform learning 



This work was partially supported by Beijing Nova Program under Grant No. Z161100004916088, the National Natural Science Foundation of China (Project 61573068, 61471048, and 61375031), and the Fundamental Research Funds for the Central Universities under Grant No. 2014ZD03-01.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jian Lin
    • 1
  • Tianyue Zheng
    • 1
  • Yanbing Liao
    • 1
  • Weihong Deng
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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