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Although accurate, two-stage face detectors usually require more inferencetime than single-stage detectors do. This paper proposes a simple yet effectivesingle-stage model for real-time face detection with a prominently highaccuracy. We build our single-stage model on the top of the ResNet-101 backboneand analyze some problems with the baseline single-stage detector in order todesign several strategies for reducing the false positive rate. The designleverages the context information from the deeper layers in order to increaserecall rate while maintaining a low false positive rate. In addition, we reducethe detection time by an improved inference procedure for decoding outputsfaster. The inference time of a VGA ($640{\times}480$) image was onlyapproximately 26 ms with a Titan X GPU. The effectiveness of our proposedmethod was evaluated on several face detection benchmarks (Wider Face, AFW,Pascal Face, and FDDB). The experiments show that our method achievedcompetitive results on these popular datasets with a faster runtime than thecurrent best two-stage practices.

Revisiting a single-stage method for face detection