  Current face or object detection methods via convolutional neural network
(such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features
based on an image pyramid. However, such a strategy increases the computational
burden for face detection. In this paper, we propose a fast face detection
method based on discriminative complete features (DCFs) extracted by an
elaborately designed convolutional neural network, where face detection is
directly performed on the complete feature maps. DCFs have shown the ability of
scale invariance, which is beneficial for face detection with high speed and
promising performance. Therefore, extracting multi-scale features on an image
pyramid employed in the conventional methods is not required in the proposed
method, which can greatly improve its efficiency for face detection.
Experimental results on several popular face detection datasets show the
efficiency and the effectiveness of the proposed method for face detection.
