EMface: Detecting Hard Faces by Exploring Receptive Field Pyraminds
- URL: http://arxiv.org/abs/2105.10104v1
- Date: Fri, 21 May 2021 03:01:37 GMT
- Title: EMface: Detecting Hard Faces by Exploring Receptive Field Pyraminds
- Authors: Leilei Cao, Yao Xiao, and Lin Xu
- Abstract summary: We propose a simple yet effective 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.
Our proposed method can accelerate the inference rate significantly while achieving state-of-the-art performance.
- Score: 10.926608043159918
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 \url{https://github.com/emdata-ailab/EMface}.
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