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Facing Face Recognition with ResNet: Round One

  • Ivan Gruber
  • Miroslav Hlaváč
  • Miloš Železný
  • Alexey Karpov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10459)

Abstract

This paper presents initial experiments of an application of deep residual network to face recognition task. We utilize 50-layer deep neural network ResNet architecture, which was presented last year on CVPR2016. The neural network was modified and then fine-tuned for face recognition purposes. The method was trained and tested on challenging Casia-WebFace database and the results were benchmarked with a simple convolutional neural network. Our experiments of classification of closed and open subset show the great potential of residual learning for face recognition.

Keywords

Face recognition Classification Neural networks Data augmentation Residual training Computer vision 

Notes

Acknowledgments

This work is supported by grant of the University of West Bohemia, project No. SGS-2016-039, by Ministry of Education, Youth and Sports of Czech Republic, project No. LO1506, by Russian Foundation for Basic Research, projects No. 15-07-04415 and 16-37-60100, and by the Government of Russian, grant No. 074-U01. Moreover, access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ivan Gruber
    • 1
    • 2
    • 3
  • Miroslav Hlaváč
    • 1
    • 2
    • 3
  • Miloš Železný
    • 1
  • Alexey Karpov
    • 3
    • 4
  1. 1.Faculty of Applied Sciences, Department of CyberneticsUWBPilsenCzech Republic
  2. 2.Faculty of Applied Sciences, NTISUWBPilsenCzech Republic
  3. 3.ITMO UniversitySt. PetersburgRussia
  4. 4.SPIIRASSt. PetersburgRussia

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