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Deep Learning - Facial Landmark Detection

A deep learning model to detect facial landmarks from images/videos. We use Keras/TensorFlow and this Dataset on Kaggle.

Disclaimer: Git LFS is used for this repository! The repo contains the dataset itself.
Check Usage on how to clone the repo and pull.

Example prediction on webcam input

Example of a labeled images from the dataset

Usage

To test the model just follow the Demo on Colab.

Otherwise:
Install Git LFS with git lfs install and clone/pull the large files with git lfs clone/pull. Unzip the data folder if you want to train a model or test on the dataset. To train the model set the parameters and execute mainTraining.py. To test or evaluate a model do the same with mainTesting.py/mainEvaluating.py

Changing the tensorflow log-level (powershell):

  • For that terminal instance: $Env:TF_CPP_MIN_LOG_LEVEL = "3"
  • Permanently: Add-Content -Path $Profile.CurrentUserAllHosts -Value '$Env:TF_CPP_MIN_LOG_LEVEL = "3"'

Models

  • V1 was trained in 43 min on a Ryzen 5 3600. (Epochs: 20, Batch size: 256, Validation split: 0.2, #Images: 7049)
  • V2 was trained in 15 min on a Colab GPU. (Epochs: 100, Batch size: 256, Validation split: 0.2, #Images: 7049)
    • Results ⁘ loss: 4.3127 - masked_mean_absolute_error: 1.5840 - masked_accuracy: 0.5411
    • Specialties ⁘ masking the ouput for the missing values
  • V3 was trained in 25 min on a Colab GPU. (Epochs: 100, Batch size: 256, Validation split: 0.2, #Images: 11329)
    • Results ⁘ loss: 3.7041 - masked_mean_absolute_error: 1.4212 - masked_accuracy: 0.6283
    • Specialties ⁘ trained with rotation augmented data
  • V4 was trained in 40 min on a Colab GPU. (Epochs: 100, Batch size: 256, Validation split: 0.2, #Images: 17749)
    • Results ⁘ loss: 4.2459 - masked_mean_absolute_error: 1.5622 - masked_accuracy: 0.6248
    • Specialties ⁘ more augmentation (rotation, horizontal flip, crop & pad, perspective, brightness & contrast)

Dataset references

Facial Keypoints Dataset

The dataset for the kaggle competition from where we have the image dataset was graciously provided by Dr. Yoshua Bengio of the University of Montreal.

300VW Dataset

To test on labeled video data we are using the 300VW dataset.

[1] J.Shen, S.Zafeiriou, G. S. Chrysos, J.Kossaifi, G.Tzimiropoulos, and M. Pantic. The first facial landmark tracking in-the-wild challenge: Benchmark and results. In IEEE International Conference on Computer Vision Workshops (ICCVW), 2015. IEEE, 2015.

[2] G. S. Chrysos, E. Antonakos, S. Zafeiriou and P. Snape. Offline deformable face tracking in arbitrary videos. In IEEE International Conference on Computer Vision Workshops (ICCVW), 2015. IEEE, 2015,

[3] G. Tzimiropoulos. Project-out cascaded regression with an application to face alignment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3659–3667, 2015.