Skip to content

Latest commit

 

History

History
44 lines (27 loc) · 1005 Bytes

README.md

File metadata and controls

44 lines (27 loc) · 1005 Bytes

real_time_face_recognition

This project can be divided into two steps:

  1. Get static training model.
  2. Use training model to recognize names in videos.

Dockerfile

  • The Dockerfile.gpu contains one Dockerfile which can run the dlib library to detect and recognize faces.

Training Step

Main Idea

  • Use Ransac to train the face model.

How to train

python3 train_cli.py train-with-video <your-video-location> <username>

Detecting Step

Main Idea

  • Detect and encode faces in the frames and save result videos.

How to recognize

python3 recognize_cli.py recognize-faces-in-video <your-video-location>

Doc

  • There is one detail report in doc folder.

Bash script example

  • test_experiment_cli.sh: Detect faces in the video and save as images
  • test_recognize_cli.sh: Recognize faces in the video and output result video
  • test_recognize_faces_in_images.sh: Recognize faces in the image
  • test_train_cli.sh: Train with video