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Age and Gender Recognition on Imbalanced Dataset of Face Images with Deep Learning

  • Dmitry YudinEmail author
  • Maksim Shchendrygin
  • Alexandr Dolzhenko
Conference paper
  • 14 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1156)

Abstract

The paper describes usage of deep neural networks based on ResNet and Xception architectures for recognition of age and gender of imbalanced dataset of face images. Described dataset collection process from open sources. Training sample contains more than 210000 images. Testing sample have more 1700 special selected face images with different ages and genders. Training data has imbalanced number of images per class. Accuracy for gender classification and mean absolute error for age estimation are used to analyze results quality. Age recognition is described as classification task with 101 classes. Gender recognition is solved as classification task with two categories. Paper contains analysis of different approaches to data balancing and their influence to recognition results. The computing experiment was carried out on a graphics processor using NVidia CUDA technology. The average recognition time per image is estimated for different deep neural networks. Obtained results can be used in software for public space monitoring, collection of visiting statistics etc.

Keywords

Age recognition Gender recognition Classification Face image Imbalanced dataset Deep neural network 

Notes

Acknowledgment

The research was made possible by Government of the Russian Federation (Agreement № 075-02-2019-967).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Moscow Institute of Physics and Technology (National Research University)MoscowRussia
  2. 2.Belgorod State Technological University named after V.G. ShukhovBelgorodRussia

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