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Face It 1.0 Game

Detector 2D or 3D face landmarks from Python

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Project description

Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates.

Build using FAN's state-of-the-art deep learning based face alignment method.

Note: The lua version is available here.

For numerical evaluations it is highly recommended to use the lua version which uses indentical models with the ones evaluated in the paper. More models will be added soon.

Features

Detect 2D facial landmarks in pictures

Detect 3D facial landmarks in pictures

Process an entire directory in one go

Detect the landmarks using a specific face detector.

By default the package will use the SFD face detector. However the users can alternatively use dlib or pre-existing ground truth bounding boxes.

Running on CPU/GPU

In order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device flag:

Please also see the examples folder

Installation

Requirements

• Python 3.5+ or Python 2.7 (it may work with other versions too)
• Linux, Windows or macOS
• pytorch (>=0.4)

While not required, for optimal performance(especially for the detector) it is highly recommended to run the code using a CUDA enabled GPU.

Binaries

From source

Install pytorch and pytorch dependencies. Instructions taken from pytorch readme. For a more updated version check the framework github page.

On Linux

On OSX

Get the PyTorch source

Install PyTorch

On Linux

On OSX

Get the Face Alignment source code

Install the Face Alignment lib

Docker image

A Dockerfile is provided to build images with cuda support and cudnn v5. For more instructions about running and building a docker image check the orginal Docker documentation.

How does it work?

While here the work is presented as a black-box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my webpage.

Contributions

All contributions are welcomed. If you encounter any issue (including examples of images where it fails) feel free to open an issue.

Citation

For citing dlib, pytorch or any other packages used here please check the original page of their respective authors.

Acknowledgements

• To the pytorch team for providing such an awesome deeplearning framework
• To my supervisor for his patience and suggestions.
• To all other python developers that made available the rest of the packages used in this repository.

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