Pytorch Face Landmark Detection

Implementation of face landmark detection with PyTorch. The model was trained using coordinate-based regression methods. A video demo was displayed here.

• Support 68-point and 39-point landmark inference.
• Support different backbone networks.
• Support ONNX inference.

Inference

Test on a sample folder and save the landmark detection results.

python3 -W ignore test_batch_mtcnn.py

Optimize with ONNX and test on a camera. The pytorch model has been converted to ONNX for fast inference.

python3 -W ignore test_camera_mtcnn_onnx.py

Benchmark Results on 300W

• Inter-ocular Normalization (ION)

Visualization Results

• Face alignment on 300W dataset

• Semi-frontal face alignment on Menpo dataset

• Profile face alignment on Menpo dataset

TODO

The following features will be added soon.

• Still to come:
  • Support for the 39-point detection
  • Support for the 106 point detection
  • Support for heatmap-based inferences

Datasets:

• 300W (68-point), Menpo (68-point), 300-VW (68-point)
• WFLW (98-point)
• JD (106-point)

References:

• https://github.com/rwightman/pytorch-image-models
• https://github.com/Res2Net/Res2Net-PretrainedModels
• https://github.com/HRNet/HRNet-Image-Classification
• https://github.com/lzx1413/pytorch_face_landmark
• https://github.com/polarisZhao/PFLD-pytorch