A few question sorry to trouble about cnn_face_detection

[anson0910]

Hi,

1. The 2 corresponds to the pooling layer of the first 12 net, since the pooling layer scales the input image down by a factor of 2.

Yes, narrowing the image means finding larger faces, since if you want to find smaller faces, you can just decrease the min_face_size argument of the detect_faces_net function.

The number of detection windows generated can be calculated as follows:
We wish to find 40 × 40 faces, so we first downscale the original image by a factor of 12/40, which results in an image of size 240 x 180, generating ((240 - 40) / 4 + 1) * ((180 - 40) / 4 + 1) = 1836 windows, and depending on the resizing factor for creating the pyramid, the number of detection windows may vary.

In 12 net it says 12 × 12 detection windows,is it because the net input is 12*12 so the window is 12?
Yes

is 4-pixel spacing corresponding to the train_val.prototxt and how the 4 is been calculated?
The spacing can be any value, because according to the description of the original paper, crops are taken out of the image and fed into the 12-net one at a time.
However, I have modified the 12-net to be a fully convolutional neural network, such that much redundant work can be saved.
You can take a look at this link if you're interested.

No welcome, I'm from Taiwan!

[tangyudi]

Thank you and why the factor is calculated by 12/40,what is the meaning of the factor?
I put a 466 x 699 image to the network after resize_image it is 139 x209 and the (out = net_12c_full_conv.blobs['prob'].data[0][1, :, :]) out.shape is 64*99, is each confidence point in 64 x99 means the possibility of a face and if so why a point can represent a rectangle?
In the paper it says the image pyramid is resized by 12/F ,is it means w x12/F,h x12/F?
I use 1W face image and 1W background image without face to train the 12net, is it enough?

[anson0910]

The factor resizes the image such that after resizing, each 12 x 12 block corresponds to min_face_size x min_face_size block in original image.

why a point can represent a rectangle?
A point in the last output feature map represents a 12 x 12 block in resized image, which in turn corresponds to min_face_size x min_face_size block in original image.

In the paper it says the image pyramid is resized by 12/F ,is it means w x12/F,h x12/F?
Yes.

I use 1W face image and 1W background image without face to train the 12net, is it enough?
Yes, I think this is enough.