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

LI Zechen; LI Hengchao; HU Wenshuai; YANG Jinyu; HUA Zexi

doi:10.3969/j.issn.0258-2724.20210017

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

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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

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.
- masked face,
- deep learning,
- attention mechanism,
- multi-scale learning,
- feature fusion,
- object detection

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References

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

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