README.md

LivenessTests

Liveness Tests For Facial Recognition. 4th year project for M.Eng Computer Science at Durham University. Supervised by Andrei Krokhin.

Introduction

The aim of this project is to understand the realm of facial liveness tests, and produce an investigation into the ease of creating a liveness as a service platform.

This project creates three new liveness tests, one for whole image assessment, one for facial structure assessment, and one for detecting mask attacks.

Dependencies:

• Python 3.5+
• Pip
• Pipenv for dependency management
• Numpy
• OpenCV
• SkImage
• Pywavelets
• Sci-kit Learn
• H5py
• Keras
• Tensorflow (either tensorflow for CPU, or tensorflow-GPU for GPU)
• face-recognition
• joblib
• pyvideoquality, v0.1.0-beta.5", from git = "https://github.com/OhmGeek/video-quality.git" (adapted to a Python library by me).

Folder Structure

app/

This contains all the source code necessary to run the program. README.md in the app folder explains what all the scripts do, and goes into detail about the namespaces.

datasets/

This needs to be a blank folder with a specific folder structure. Why? It's where the cached datasets are stored (in h5 format). This is done to improve performance.

Under the datasets folder, 3 folders are needed. The files shown below might not exist, but they will be created when running the application.

datasets/
    mad/
        mad.h5
    nuaa/
        nuaa.h5
    replay-attack/
        replayAttackdevel.h5
        replayAttacktest.h5

models/

This is where functional models are kept. CNN and VoxNet used h5 to store weights, while the WIQA method uses pickle (because it's simple and easy).

How to run?

Navigate to the app folder.

Install pipenv and Python3.7 then run:

pipenv install
pipenv shell

Then run the python script as normal. A virtualenv with tensorflow CPU will be created for use.