  Modern convolutional neural networks (CNNs)-based face detectors have
achieved tremendous strides due to large annotated datasets. However,
misaligned results with high detection confidence but low localization accuracy
restrict the further improvement of detection performance. In this paper, the
authors first predict high confidence detection results on the training set
itself. Surprisingly, a considerable part of them exist in the same
misalignment problem. Then, the authors carefully examine these cases and point
out that annotation misalignment is the main reason. Later, a comprehensive
discussion is given for the replacement rationality between predicted and
annotated bounding-boxes. Finally, the authors propose a novel Bounding-Box
Deep Calibration (BDC) method to reasonably replace misaligned annotations with
model predicted bounding-boxes and offer calibrated annotations for the
training set. Extensive experiments on multiple detectors and two popular
benchmark datasets show the effectiveness of BDC on improving models' precision
and recall rate, without adding extra inference time and memory consumption.
Our simple and effective method provides a general strategy for improving face
detection, especially for light-weight detectors in real-time situations.
