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The Study of Improving the Accuracy of Convolutional Neural Networks in Face Recognition Tasks

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12665)

Abstract

The article discusses the efficiency of convolutional neural networks in solving the problem of face recognition of tennis players. The characteristics of training and accuracy on a test set for networks of various architectures are compared. Application of weight drop out methods and data augmentation to eliminate the effect of retraining is also considered. Finally, the transfer learning from other known networks is used. It is shown how, for initial data, it is possible to increase recognition accuracy by 25% compared to a typical convolutional neural network.

Keywords

  • Recognition
  • Convolutional neural networks
  • Regularization
  • Drop out
  • Augmentation
  • Learning transfer
  • Cats vs Dogs
  • Federer vs Nadal

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Acknowledgement

The study was funded by RFBR, Project № 19-29-09048, RFBR and Ulyanovsk Region, Project № 19-47-730011.

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Correspondence to Nikita Andriyanov .

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Andriyanov, N., Dementev, V., Tashlinskiy, A., Vasiliev, K. (2021). The Study of Improving the Accuracy of Convolutional Neural Networks in Face Recognition Tasks. In: , et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-68821-9_1

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