Skip to content

Latest commit

 

History

History
37 lines (24 loc) · 1.18 KB

README.md

File metadata and controls

37 lines (24 loc) · 1.18 KB

FaceNet Implementation:

Face Recognition system trained using FaceNet for 81 people. Accuracy is around 70% achieved.

Details Explanation of FaceNet: here

facenet arc result

Click here for Details Explanation in Burmese YouTube Video

Real-time test result:

real-time test result

Steps on how to run:

  1. Download miniconda3 and install.

  2. Install conda evn and required pip library packages and go the environment.

conda env create -f environment.yml
pip install -r requirement.txt
conda activate CV
  1. (Optional) If want to train on own image, follow the instructions from Facenet.

  2. Run the test on single image.

python src/image.py --img_filename [PATH OF TEST IMAGE] --save_filename [NAME TO SAVE RESULT]
  1. Run the real time video.
python src/video.py --video_link [0(WEB CAM) or 1(USB CAM) ]