Understanding cascading of sizes in mtcnn about mtcnn

[DuinoDu]

Hey, thanks for your asking. As PNet is a fully convolution network, input size is not limited. So 37x17 is OK for the input of PNet. As for 12x12 in det1.prototxt, it is just an initialization of the network, not standing for actual image input size.

[sidgan]

Could you please explain how this works? How does the network scale to each input size ?

[DuinoDu]

It's much like rpn in fasterRCNN. The network doesn't scale each input size to a fixed size, since there is no fully connected layer in network. 12x12 is used in https://github.com/DuinoDu/mtcnn/blob/master/demo.py#L153
That means for any input size, PNet first generates feature maps. This feature maps can be any size. And then, use a sliding window to generate fixed size seed boxes, as you know, 12x12. This work is done in https://github.com/DuinoDu/mtcnn/blob/master/demo.py#L151
def generateBoundingBox(map, reg, scale, t). Then these seed boxes are moved closer to target, using bounding box regression. Besides, use the third branch---facial landmark localization to improve accuracy of face detection.