  The small receptive field and capacity of minimal neural networks limit their
performance when using them to be the backbone of detectors. In this work, we
find that the appearance feature of a generic face is discriminative enough for
a tiny and shallow neural network to verify from the background. And the
essential barriers behind us are 1) the vague definition of the face bounding
box and 2) tricky design of anchor-boxes or receptive field. Unlike most
top-down methods for joint face detection and alignment, the proposed KPNet
detects small facial keypoints instead of the whole face by in a bottom-up
manner. It first predicts the facial landmarks from a low-resolution image via
the well-designed fine-grained scale approximation and scale adaptive
soft-argmax operator. Finally, the precise face bounding boxes, no matter how
we define it, can be inferred from the keypoints. Without any complex head
architecture or meticulous network designing, the KPNet achieves
state-of-the-art accuracy on generic face detection and alignment benchmarks
with only $\sim1M$ parameters, which runs at 1000fps on GPU and is easy to
perform real-time on most modern front-end chips.
