BDC: Bounding-Box Deep Calibration for High Performance Face Detection.
2021
Modern CNN-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, we first generate detection results on training set
itself. Surprisingly, a considerable part of them exist the same misalignment
problem. Then, we carefully examine these misaligned cases and point out
annotation inconsistency is the main reason. Finally, we propose a novel
Bounding-Box Deep Calibration (BDC) method to reasonably replace inconsistent
annotations with model predicted bounding-boxes and create a new annotation
file for training set. Extensive experiments on WIDER FACE dataset show the
effectiveness of BDC on improving models' precision and recall rate. Our simple
and effective method provides a new direction for improving face detection.
Source code is available at https://github.com/shiluo1990/BDC.
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