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DynFace: A Multi-label, Dynamic-Margin-Softmax Face Recognition Model

  • Marius CordeaEmail author
  • Bogdan Ionescu
  • Cristian Gadea
  • Dan Ionescu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)

Abstract

Convolutional neural networks (CNN), more recently, have greatly increased the performance of face recognition due to its high capability in learning discriminative features. Many of the initial face recognition algorithms reported high performance in the small size Labeled Faces in the Wild (LFW) dataset but fail to deliver same results on larger or different datasets. Ongoing research tries to boost the performance of Face Recognition methods by modifying either the neural network structure or the loss function. This paper proposes two novel additions to the typical softmax CNN used for face recognition: a fusion of facial attributes at feature level and a dynamic margin softmax loss. The new network DynFace was extensively evaluated on extended LFW and much larger MegaFace, comparing its performance against known algorithms. The DynFace achieved state-of-art accuracy at high speed. Results obtained during the carefully designed test experiments, are presented in the end of this paper.

Keywords

Face recognition Face verification Face identification Convolutional neural networks Deep learning Multi-label Softmax Additive margin softmax 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marius Cordea
    • 1
    Email author
  • Bogdan Ionescu
    • 1
  • Cristian Gadea
    • 2
  • Dan Ionescu
    • 2
  1. 1.Mgestyk TechnologiesOttawaCanada
  2. 2.University of OttawaOttawaCanada

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