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The Video-Based Age and Gender Recognition with Convolution Neural Networks

  • Angelina S. KharchevnikovaEmail author
  • Andrey V. Savchenko
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 247)

Abstract

The paper reviews the problem of age and gender recognition methods for video data using modern deep convolutional neural networks. We present the comparative analysis of classifier fusion algorithms to aggregate decisions for individual frames. We implemented the video-based recognition system with several aggregation methods to improve the age and gender identification accuracy. The experimental comparison of the proposed approach with traditional simple voting using IJB-A, Indian Movies, and Kinect datasets is provided. It is demonstrated that the most accurate decisions are obtained using the geometric mean and mathematical expectation of the outputs at softmax layers of the convolutional neural networks for gender recognition and age prediction, respectively.

Keywords

Age and gender recognition Contextual advertising Convolutional neural networks Classifier fusion 

Notes

Acknowledgements

The paper was prepared within the framework of the Academic Fund Program at the National Research University Higher School of Economics (HSE) in 2017 (grant 17-05-0007) and by the Russian Academic Excellence Project “5–100”. Andrey V. Savchenko is partially supported by Russian Federation President grant no. MD-306.2017.9.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Angelina S. Kharchevnikova
    • 1
    Email author
  • Andrey V. Savchenko
    • 1
    • 2
  1. 1.National Research University Higher School of EconomicsNizhniy NovgorodRussia
  2. 2.Laboratory of Algorithms and Technologies for Network AnalysisNational Research University Higher School of EconomicsNizhny NovgorodRussia

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