demo-code

• Contains code files for the project Face recognition using Haar-cascade.

Usage (python3)

1. Create a new folder names: project_face_recog. Copy all files from the repository to the created folder
2. Open cmd in the folder.
3. Install dependencies

pip install -r requirements.txt

4. Test whether opencv can detect camera.

python demo_cam_test.py

5. Make sure the .xml is present in the directory, check if face detection is working:

python demo_face_detection.py

6. Start building dataset for training purposes.
7. Create a dataset folder in the same directory as the script.
8. To build a dataset for your face for recognition purposes.

python face_dataset.py

• Inputs: face_id (int).
• Program will ask for an input.

Enter user id end press <return> ==> 1

• Can repeat this step for multiple faces, just give a different id (sequential)
• This will take 30 images of your face and save them in the dataset folder
• At the end, the datasets folder will be opened, delete bad images.

9. Create a trainer folder in the same directory as script.
10. After dataset has been created, train a classifier on your face images.

• First update line 16

names = ['None'] # add your name corresponding to the id number like, 
# names = ['None', 'Vaibhav', ...]

python face_training.py

11. After training, for recognition demo, execute:

python face_recognition.py

Main Reference:

Real-Time Face Recognition: An End-To-End Project