README.md

Welcome to face-landmark-detector 👋

This is a part of Mock-Buddy project, used to detect face interactivity. CNN architecture used to build the model to detect facial landmarks. The model is build with TensorFlow applying direction regression apporach.

Prerequisite

• Python 3.7 or newer

Dataset

300W consists of several datasets

• iBUG
• AFW
• HELEN
• LFPW

You need bounding boxes to crop faces from above datasets

• Bounding-box-annotations

Usage

After downloading datasets just update the extraction paths with your datasets path.

• Export train and test csv from 300W.ipynb.
• Run model_train.ipynb to start training.

I have taken test datasets as iBug, Helen-test and LFPW-test and used evaluation metrics mentioned in 300 faces in-the-wild challenge (link).

Trained model metrics are in metrics folder.

Author

👤 Karthick T. Sharma

• Github: @Karthick47v2
• LinkedIn: @Karthick47

Todo

• Add heatmap regression approach

Citation

@article{sagonas2016300,
  title={300 faces in-the-wild challenge: Database and results},
  author={Sagonas, Christos and Antonakos, Epameinondas and Tzimiropoulos, Georgios and Zafeiriou, Stefanos and Pantic, Maja},
  journal={Image and Vision Computing},
  volume={47},
  pages={3--18},
  year={2016},
  publisher={Elsevier}
}
@inproceedings{sagonas2013300,
  title={300 faces in-the-wild challenge: The first facial landmark localization challenge},
  author={Sagonas, Christos and Tzimiropoulos, Georgios and Zafeiriou, Stefanos and Pantic, Maja},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
  pages={397--403},
  year={2013},
  organization={IEEE}
}

🤝 Contributing

Contributions, issues and feature requests are welcome!
Feel free to check issues page.

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