FaceQnet

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

2020.07.02: 更新最新的人脸质量评价模型

This repository contains the DNN FaceQnet presented in the paper: "FaceQnet: Quality Assessment for Face Recognition based on Deep Learning".

FaceQnet is a No-Reference, end-to-end Quality Assessment (QA) system for face recognition based on deep learning. The system consists of a Convolutional Neural Network that is able to predict the suitability of a specific input image for face recognition purposes. The training of FaceQnet is done using the VGGFace2 database.

-- Configuring environment in Windows:

1. Installing Conda: https://conda.io/projects/conda/en/latest/user-guide/install/windows.html

Update Conda in the default environment:

conda update conda
conda upgrade --all

Create a new environment:

conda create -n [env-name]

Activate the environment:

conda activate [env-name]

2. Installing dependencies in your environment:

Install Tensorflow and all its dependencies:

pip install tensorflow

Install Keras:

pip install keras

Install OpenCV:

conda install -c conda-forge opencv

3. If you want to use a CUDA compatible GPU for faster predictions:

You will need CUDA and the Nvidia drivers installed in your computer: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/

Then, install the GPU version of Tensorflow:

pip install tensorflow-gpu

-- Using FaceQnet for predicting scores: 2) Due to the size of the video example, please download the FaceQnet pretrained model here (.h5 file) and place it in the /src folder.