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Face Recognition Based on Improved FaceNet Model

  • Qiuyue Wei
  • Tongjie Mu
  • Guijin HanEmail author
  • Linli Sun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

The convolutional neural networks (CNN) is one of the most successful deep learning model in the field of face recognition, the different image regions are always treated equally when extracting image features, but in fact different parts of the face play different roles in face recognition. For overcoming this defect, a weighted average pooling algorithm is proposed in this paper, the different weights are assigned to the abstract features from different local image regions in the pooling operation, so as to reflect its different roles in face recognition. The weighted average pooling algorithm is applied to the FaceNet network, and a face recognition algorithm based on the improved FaceNet model is proposed. The simulation experiments show that the proposed face recognition algorithm has higher recognition accuracy than the existing face recognition methods based on deep learning.

Keywords

Face recognition Deep learning FaceNet Convolutional neural networks Weighting coefficient 

Notes

Acknowledgements

This work was supported by the Shaanxi Natural Science Foundation (2016JQ5051) and the Department of Education Shaanxi Province (2013JK1023).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Xi’an University of Posts and TelecommunicationsXi’anChina

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