Face Recognition with InsightFace

Recognize and manipulate faces with Python and its support libraries.
The project uses MTCNN for detecting faces, then applies a simple alignment for each detected face and feeds those aligned faces into embeddings model provided by InsightFace. Finally, a softmax classifier was put on top of embedded vectors for classification task.

Getting started

Requirements

• Python 3.3+
• Virtualenv
• python-pip
• mx-net
• tensorflow
• macOS or Linux

Installing

Check update:

sudo apt-get update

Install python:

sudo apt-get install python3.6

Install pip:

sudo apt install python3-pip

Most of the necessary libraries were installed and stored in env/ folder, so what we need is installing virtualenv to use this enviroment.
Install virtualenv:

sudo pip3 install virtualenv virtualenvwrapper

Usage

First, go to directory that you have cloned, activate env to use installed package, alternatively, you must install all packages that necessary for this project.

source env/bin/activate

Now move to /src folder and try out first recognition with this command:

python3 recognizer_image.py 

The result should be something like this You can also try with recognition in video with:

python3 recognizer_video.py

see full video here Game of Thrones

or streaming if your machine supports camera:

python3 recognizer_stream.py

Follow this link to see an example of video streaming Streaming

Build your own faces recognition system

By default, most of the input and output arguments were provided, models and embeddings is set default stored in /src/outputs/.

1. Prepare your data

Our training datasets were built as following structure:

/datasets
  /train
    /person1
      + face_01.jpg
      + face_02.jpg
      + ...
    /person2
      + face_01.jpg
      + face_02.jpg
      + ...
    / ...
  /test
  /unlabeled_faces
  /videos_input
  /videos_output

In each /person_x folder, put your face images corresponding to person_name that has been resized to 112x112 (input size for InsightFace). Here I provided two ways to get faces data from your webcam and video stored in your storage.
a. Get faces from camera
Run following command, with --faces defines how many faces you want to get, default is 20

python3 get_faces_from_camera.py [--faces 'num_faces'] [--output 'path/to/output/folder']

Here [--cmd] means cmd is optional, if not provide, script will run with its default settings.
b. Get faces from video
Prepare a video that contains face of the person you want to get and give the path to it to --video argument:

python3 get_faces_from_video.py [--video 'path/to/input/video'] [--output 'path/to/output/folder']

As I don't provide stop condition to this script, so that you can get as many faces as you want, you can also press q button to stop the process.

The default output folder is /unlabeled_faces, select all faces that match the person you want, and copy them to person_name folder in train. Do the same things for others person to build your favorite datasets.

2. Generate face embeddings

python3 faces_embedding.py [--dataset 'path/to/train/dataset'] [--output 'path/to/out/put/model']

3. Train classifier with softmax

python3 train_softmax.py [--embeddings 'path/to/embeddings/file'] [--model 'path/to/output/classifier_model'] [--le 'path/to/output/label_encoder']

4. Run

Yep!! Now you have a trained model, let's enjoy it!
Face recognization with image as input:

python3 recognizer_image.py [--image-in 'path/to/test/image'] [...]

Face recognization with video as input:

python3 recognizer_video.py [--video 'path/to/test/video'] [...]

Face recognization with camera:

python3 recognizer_stream.py

[...] means other arguments, I don't provide it here, you can look up in the script at arguments part

Others

Using gpu for better performance

I use CPU for all recognition tasks for mxnet haven't supported for cuda in Ubuntu 18.10 yet. But if your machine has an Nvidia GPU and earlier version of Ubuntu, you can try it out for better performance both in speed and accuracy. In my case, I have changed line 46 in face_model ctx = mx.cpu(0) to use cpu.

Thanks

• Many thanks to Davis King for creating dlib with lots of helpful function in face deteting, tracking and recognizing
• Thanks to everyone who works on all the awesome Python data science libraries like numpy, scipy, scikit-image, pillow, etc, etc that makes this kind of stuff so easy and fun in Python.
• Thanks to Jia Guo and Jiankang Deng for their InsightFace project
• Thanks to Adrian Rosebrock for his useful tutorials in pyimagesearch that help me a lots in building this project.