  Although accurate, two-stage face detectors usually require more inference
time than single-stage detectors do. This paper proposes a simple yet effective
single-stage model for real-time face detection with a prominently high
accuracy. We build our single-stage model on the top of the ResNet-101 backbone
and analyze some problems with the baseline single-stage detector in order to
design several strategies for reducing the false positive rate. The design
leverages the context information from the deeper layers in order to increase
recall rate while maintaining a low false positive rate. In addition, we reduce
the detection time by an improved inference procedure for decoding outputs
faster. The inference time of a VGA ($640{\times}480$) image was only
approximately 26 ms with a Titan X GPU. The effectiveness of our proposed
method was evaluated on several face detection benchmarks (Wider Face, AFW,
Pascal Face, and FDDB). The experiments show that our method achieved
competitive results on these popular datasets with a faster runtime than the
current best two-stage practices.
