Feature map masking based single-stage face detection

Xi Zhang, Junliang Chen, Weicheng Xie, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Although great progress has been made in face detection, a trade-off between speed and accuracy is still a great challenge. We propose in this paper a feature map masking based approach for single-stage face detection. As feature maps extracted from feature pyramid network might contain face unrelated features, we propose a mask generation branch to predict those significant units for face detection. The masked feature maps, where only important features are left, are then passed through the following detection process. Ground truth masks, directly generated from the training images, based on the face bounding boxes, are used to train the feature mask generation module. A mask constrained dropout module has also been proposed to drop out significant units of the shared feature maps, such that the detection performance can be further improved. The proposed approach is extensively tested using the WIDER FACE dataset. The results suggest that our detector with ResNet-152 backbone, achieves the best precision-recall performance among competing methods. As high as 95.4%, 94.0% and 86.9% accuracies have been achieved on the easy, medium and hard subsets, respectively.

Original languageEnglish
Title of host publicationIJCB 2020 - IEEE/IAPR International Joint Conference on Biometrics
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728191867
Publication statusPublished - 28 Sept 2020
Externally publishedYes
Event2020 IEEE/IAPR International Joint Conference on Biometrics, IJCB 2020 - Virtual, Online, United States
Duration: 28 Sept 20201 Oct 2020

Publication series

NameIJCB 2020 - IEEE/IAPR International Joint Conference on Biometrics


Conference2020 IEEE/IAPR International Joint Conference on Biometrics, IJCB 2020
Country/TerritoryUnited States
CityVirtual, Online

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Instrumentation


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