13 lines
998 B
Text
13 lines
998 B
Text
|
To create the training data run:
|
||
|
|
Create_data_68_large.m or Create_data_66_large.m for 68 and 66 point PDM versions accordingly.
|
||
|
|
|
||
|
|
The data generation code requires you to have the patch expert training data (Multi-PIE and in-the-wild data, not included) for positive examples, and inriaperson dataset for negative samples (not included as well).
|
||
|
|
|
||
|
|
To train Convolutional Neural Network based face landmark validation model (used by the model now) use:
|
||
|
|
Train_face_checker_66_cnn.m and Train_face_checker_68_cnn.m
|
||
|
|
|
||
|
|
This will produce trained/face_checker_cnn_*.mat and trained/face_checker_cnn_*.txt files that can be used in C++ and matlab versions of CLM framework for face checking.
|
||
|
|
|
||
|
|
This will also produces tris*.txt files that can be used in the C++ version of the CLM_framework.
|
||
|
|
|
||
|
|
The code uses piece-wise affine warping to a neutral shape with an CNN regressor for error estimation (see http://www.cl.cam.ac.uk/~tb346/ThesisFinal.pdf Section 4.6.2 for a very similar model but with SVR regressor)
|