###Face Alignment This program is a C++ reimplementation of algorithms in paper "Face Alignment by Explicit Regression" by Cao et al. This program can be used to train models for detecting facial keypoints, and it is extremely fast both in training and testing.
Please go to folder
FaceAlignment to see the source files.
- Nov 13, 2014 Improve the speed of model training. Now it takes about 40 min to train a model on 1345 images with 20 initial for each on a Core i7 3.40 GHz CPU. Considering no parallel programing is used, this performance is acceptable.
###Usage To compile the program(OpenCV required):
// Go to folder FaceAlignment cmake . make TrainDemo.out make TestDemo.out
To train a new model:
ShapeRegressor regressor; regressor.Train(images,ground_truth_shapes,bounding_box,first_level_num,second_level_num, candidate_pixel_num,fern_pixel_num,initial_number); regressor.Save("./data/model.txt");
To predict a new input:
ShapeRegressor regressor; regressor.load("./data/model_cofw_2.txt"); regressor.Predict(test_images[index],bounding_box[index],initial_number);
For details, please see
A public dataset is provided here. The dataset contains 1345 training images, 507 testing images, and each image has 29 landmarks. You can change the path
TestDemo.cpp to train new models.
###Model I have prepared a model trained by me on COFW dataset, and you can access it here.
How to get the bounding box of an input face image? You can get the bounding box with a face detector, which has been implemented in OpenCV. However, do remember that, if you use the model provided by me, you must provide a bounding box of similar measure with the training data. Otherwise, the result will be poor. If the bounding box of training data is very small, but you provide a very big bounding box for testing data, it is certain that you will get a poor result. Here the same measure doesn't mean that they have to be the same size, but they have to be taken using the same standard, for example, the ratio between bounding box width and the two-eye distance should be the same.
It seems that there are still some errors when I do testing, so is there any problem? Generally speaking, the dataset attached is very challenging because it includes heavy occlusions and large shape variations. You could try other standard datasets such as Helen and LFW, which should lead to better performance.
boundingbox.txt, each row is in the following format, specifying the bounding box of a face in the corresponding image:
x // x coordinates of top-left corner y // y coordinates of top-right corner width height
keypoints.txt, each row is in the following format, specifying the ground truth of keypoints locations:
x_1 x_2 ... x_N y_1 y_2 ... y_N
###Contact If you have any question about the code, I prefer that you create an issue on GitHub rather than send me emails directly, so that others can also refer to it when they have problems. I will respond to it as soon as possible.
###Reference papers: Face Alignment by Explicit Shape Regression