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Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling

  • Zichang Tan
  • Shuai Zhou
  • Jun WanEmail author
  • Zhen Lei
  • Stan Z. Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)

Abstract

In this paper, we propose a novel approach based on a single convolutional neural network (CNN) for age estimation. In our proposed network architecture, we first model the randomness of aging with the Gaussian distribution which is used to calculate the Gaussian integral of an age interval. Then, we present a soft softmax regression function used in the network. The new function applies the aging modeling to compute the function loss. Compared with the traditional softmax function, the new function considers not only the chronological age but also the interval nearby true age. Moreover, owing to the complex of Gaussian integral in soft softmax function, a look up table is built to accelerate this process. All the integrals of age values are calculated offline in advance. We evaluate our method on two public datasets: MORPH II and Cross-Age Celebrity Dataset (CACD), and experimental results have shown that the proposed method has gained superior performances compared to the state of the art.

Keywords

Face Image Local Binary Pattern Convolutional Neural Network Mean Absolute Error Stochastic Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by the National Key Research and Development Plan (Grant No. 2016YFC0801002), the Chinese National Natural Science Foundation Projects \(\sharp \)61473291, \(\sharp \)61572501, \(\sharp \)61502491, \(\sharp \)61572536, Science and Technology Development Fund of Macau (No. 019/2014/A1), NVIDIA GPU donation program and AuthenMetric \( R \& D\) Funds.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zichang Tan
    • 1
    • 2
  • Shuai Zhou
    • 1
    • 3
  • Jun Wan
    • 1
    • 2
    Email author
  • Zhen Lei
    • 1
    • 2
  • Stan Z. Li
    • 1
    • 2
  1. 1.National Laboratory of Pattern Recognition, Center for Biometrics and Security ResearchInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Faculty of Information TechnologyMacau University of Science and TechnologyTaipaMacau

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