mlsanityData to 14 Oct 2026

EMface: Detecting Hard Faces by Exploring Receptive Field Pyraminds

arXiv 2105.10104 ↗First posted 21 May 2021cs.CV cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

Scale variation is one of the most challenging problems in face detection. Modern face detectors employ feature pyramids to deal with scale variation. However, it might break the feature consistency across different scales of faces. In this paper, we propose a simple yet effective method named the receptive field pyramids (RFP) method to enhance the representation ability of feature pyramids. It can learn different receptive fields in each feature map adaptively based on the varying scales of detected faces. Empirical results on two face detection benchmark datasets, i.e., WIDER FACE and UFDD, demonstrate that our proposed method can accelerate the inference rate significantly while achieving state-of-the-art performance. The source code of our method is available at urlhttps://github.com/emdata-ailab/EMface.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 26 references that are in the map. First posted on arXiv 21 May 2021.

Builds on

23 of 26 references.

References · 23

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  12. 2015WIDER FACE: A Face Detection Benchmarkcited by 298
11 more
  1. 2017S3FD: Single Shot Scale-invariant Face Detectorcited by 142
  2. 2017Receptive Field Block Net for Accurate and Fast Object Detectioncited by 141
  3. 2019Feature Selective Anchor-Free Module for Single-Shot Object Detectioncited by 139
  4. 2018Look at Boundary: A Boundary-Aware Face Alignment Algorithmcited by 138
  5. 2018DSFD: Dual Shot Face Detectorcited by 116
  6. 2018PyramidBox: A Context-assisted Single Shot Face Detectorcited by 58
  7. 2017SSH: Single Stage Headless Face Detectorcited by 40
  8. 2018Pose-Robust Face Recognition via Deep Residual Equivariant Mappingcited by 28
  9. 2018Selective Refinement Network for High Performance Face Detectioncited by 21
  10. 2018Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Resultscited by 16
  11. 2018SFace: An Efficient Network for Face Detection in Large Scale Variationscited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 1.

2 papers.

  1. 2021Improved YOLOv5 network for real-time multi-scale traffic sign detectioncited by 11
  2. 2022EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detectioncited by 6