This is the implementation of face landmark detection on 300-W dataset using caffe.
The success of landmark detection mainly relies on two aspects: (a) Data Augmentation and (B) Network. Differing than the above implementations, I focus on the 68 point landmark annotation, which is more challenging than 5 point landmark annotation. This experiment is purely trained on the 300W dataset itself, without using any external dataset. For face detection, I use DLIB library. Please install caffe and dlib ahead before playing with this model.
For data augmentation, I use both rotation and bounding box perturbation. After data augmentation, there is a total of 30,301 samples and 5,878 samples for training and validation sets, respectively.
For network, I choose Vanilla CNN as the building block. The input size is 40*40 and the landmark positions has been scaled to [0,1].
python predict_vanilla_fd_one.py Model_68Point/_iter_1400000.caffemodel 314.jpg
Images are either taken from the face landmark evaluation dataset or from the Internet. Copyright belongs to the owners.
The implementation is inspired from the following projects.
References: