• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
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Volume 56 Issue 5
Oct.  2021
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Journal of Southwest Jiaotong University  > 2021  >  56(5): 1002-1010.
LI Zechen, LI Hengchao, HU Wenshuai, YANG Jinyu, HUA Zexi. Masked Face Detection Model Based on Multi-scale Attention-Driven Faster R-CNN[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 1002-1010. doi: 10.3969/j.issn.0258-2724.20210017
Citation: LI Zechen, LI Hengchao, HU Wenshuai, YANG Jinyu, HUA Zexi. Masked Face Detection Model Based on Multi-scale Attention-Driven Faster R-CNN[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 1002-1010. doi: 10.3969/j.issn.0258-2724.20210017
shu

Masked Face Detection Model Based on Multi-scale Attention-Driven Faster R-CNN

doi: 10.3969/j.issn.0258-2724.20210017
  • Received Date: 11 Jan 2021
  • Rev Recd Date: 07 Jul 2021
    • Available Online: 16 Jul 2021
  • Publish Date: 15 Oct 2021
    Abstract
  • For the purpose of masked face detection, a multi-scale attention-driven faster region-based convolutional neural network (MSAF R-CNN) model is proposed. First, given the Faster R-CNN model architecture and the multi-scale information of the face, Res2Net, a grouped-residual structure, is introduced to model more fine-grained features. Then, inspired by the attention mechanism, a novel spatial-channel attention Res2Net (SCA-Res2Net) module is developed to learn the multi-scale features adaptively. Finally, to further learn the global feature representation and ease the overfitting problem, the weighted spatial pyramid pooling network is embedded on the top of the model, which can segment the feature maps into different groups from finer to coarser scales. Experimental results on the AIZOO and FMDD datasets show that the accuracy of masked face detection with the proposed MSAF R-CNN model can reach 90.37% and 90.11%, respectively, thus verifying the feasibility and effectiveness of the proposed model.

     

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