  In recent years, deep convolutional neural networks (CNN) have significantly
advanced face detection. In particular, lightweight CNNbased architectures have
achieved great success due to their lowcomplexity structure facilitating
real-time detection tasks. However, current lightweight CNN-based face
detectors trading accuracy for efficiency have inadequate capability in
handling insufficient feature representation, faces with unbalanced aspect
ratios and occlusion. Consequently, they exhibit deteriorated performance far
lagging behind the deep heavy detectors. To achieve efficient face detection
without sacrificing accuracy, we design an efficient deep face detector termed
EfficientFace in this study, which contains three modules for feature
enhancement. To begin with, we design a novel cross-scale feature fusion
strategy to facilitate bottom-up information propagation, such that fusing
low-level and highlevel features is further strengthened. Besides, this is
conducive to estimating the locations of faces and enhancing the descriptive
power of face features. Secondly, we introduce a Receptive Field Enhancement
module to consider faces with various aspect ratios. Thirdly, we add an
Attention Mechanism module for improving the representational capability of
occluded faces. We have evaluated EfficientFace on four public benchmarks and
experimental results demonstrate the appealing performance of our method. In
particular, our model respectively achieves 95.1% (Easy), 94.0% (Medium) and
90.1% (Hard) on validation set of WIDER Face dataset, which is competitive with
heavyweight models with only 1/15 computational costs of the state-of-the-art
MogFace detector.
