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An open-source repository for our group's academic perception projects. Current applications include facial presentation-attack detection and semantic-segmentation.

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AHassani92/Face-Perception

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Face-Perception

An open-source repository for our group's academic perception projects. This work is intended to for non-commercial research. If you use this repository, please cite the appropriate papers!

The following utilities are currently supported:

  • Synthetic noise augmentation

Noise Generators

To use the noise generators, clone the noise directory and modify the noise_faces.py script to match your data setup. Note that this uses the functions in the noise_generators_camera.py and noise_generators_environment.py files. Details are provided in the function comments.

Sensor Noises

The following sensor noises are implimented:

  • poor_focus - sensor is blurry due to poor focus
  • dark_noise - photoreceptor leakage in the form of gaussian noise
  • shot_noise - randomized photon distribution as poisson function
  • salt_and_pepper - randomized analog to digital binarization error
  • under_expose - sensor does not expose sufficiently causing loss of features
  • over_expose - sensor exposes too much, saturating out features

Environment Noises

The following environmental noises are implimented:

  • point_source - point source presents randomized saturated blob in image
  • point_shadow - small object presents randomized under exposed blob in image
  • streak_source - overhead source (e.g., sun) illuminates streak over top of image
  • streak_shadow - below horizon source (e.g., sun) illuminates streak over bottom of image
  • pipe_source - adjacent source (e.g., sun) illuminates pipe across middle of image
  • pipe_shadow - adjacent source (e.g., sun) is obstructed and casts shadow across middle of image

Implimenting Noise Augmentations

The noise functions can be directly implimented by simply importing the functions into your data loader. Alternatively, a helper function is provided in the noise_faces.py file. This file provides scripts to both add noise (using multiprocessing for speed) or remove the images. Note, you will need to verify the parser matches your repository structure.

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An open-source repository for our group's academic perception projects. Current applications include facial presentation-attack detection and semantic-segmentation.

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