Pretrained LDCNet about ldcnet HOT 6 OPEN

Hello @angelabr ,
Which protocol of the pretrained model do you need?
Which pretrained model do you need, FE and CF or the whole LDCNet?

from ldcnet.

Thank you for your prompt reply.
If possible, I would need the [O,C,I]->M protocol and the whole LDCNet.

from ldcnet.

Due to the limitation on the size of the uploaded file, I have placed it on google drive.
https://drive.google.com/drive/folders/1aQ2J1hGgJg5tsIKV26xWiIDExcyPzkq-?usp=share_link (valid until Feb 28, 2023)
I hope it will be helpful to you.

from ldcnet.

Thank you very much.
One last thing, is it necessary to perform some preprocessing on the images? I'm testing on a subset of Celeb a Spoof cropped with MTCNN and it seems to predict "1" all the time.

from ldcnet.

We use the MTCNN to preprocess the dataset, which should not pose a problem.
I think there are two potential issues:

1. We conduct cross-domain testing using only the O+C+I dataset, which consists of 7,740 images. This may not be sufficient for the larger CelebA-Spoof dataset, which includes 625,537 images. In addition, the CelebA-Spoof dataset contains a wider range of attack types, including print, replay, paper cut and 3D mask. In contrast, the O, C, I and M dataset only includes print and replay.
2. After obtaining the predictions, we follow previous methods (SSAN, CDC) to use Youden Index Calculation for obtaining the threshold of binary classification.

from ldcnet.

Thank you very much. One last thing, is it necessary to perform some preprocessing on the images? I'm testing on a subset of Celeb a Spoof cropped with MTCNN and it seems to predict "1" all the time.

Thank you very much. One last thing, is it necessary to perform some preprocessing on the images? I'm testing on a subset of Celeb a Spoof cropped with MTCNN and it seems to predict "1" all the time.

First take dataset -> take frames/images -> detect face -> crop face -> make .npy for each cropped face (be careful to BGRtoRGB and while converting normilize each image npy value / 255 (0~1) and save) -> all saved npy files are lists so save all npy files into one .npy file (list) -> THEN just use as it is

from ldcnet.