Less is More: Facial Landmarks can Recognize a Spontaneous Smile (BMVC 2022)

MeshSmileNet PyTorch Implementation

Dependency

• python 3.8
• numpy 1.21.5
• Pillow 9.0.1
• dlib 19.24.0
• opencv-python 4.6.0.66
• torch 1.11.0
• torchvision 0.12.0
• vidaug 1.5
• einops 0.6.0
• tqdm 4.64.1
• colorama 0.4.6

Dataset

• obtain the whole UVA-NEMO database from https://www.uva-nemo.org
• obtain the whole MMI database from https://mmifacedb.eu
• obtain the whole BBC database from https://www.bbc.co.uk/science/humanbody/mind/surveys/smiles/
• obtain the whole SPOS database from https://www.oulu.fi/cmvs/node/41317

Train MeshSmileNet

• smile_point.py contains the train program for MeshSmileNet.

• run python smile_point.py --fold 0 for the sample training of UVA-NEMO databases. Note only landmarks data from UVA-NEMO are provided. Other can be extracted by following the code from here https://google.github.io/mediapipe/solutions/face_mesh.html

• The model weight will be saved in uva/labels folder.

The Github Repo is under construction.