This project uses MediaPipe Face Mesh and OpenCV to detect multiple faces in real-time from a webcam feed. It demonstrates how to adjust the maximum number of faces detected and test the performance of the model.

• Detects multiple faces in real-time using MediaPipe Face Mesh.
• Displays facial landmarks on each detected face.
• Calculates and displays frames per second (FPS).
• Adjustable maximum number of faces to detect.
• Resizes frames to test detection performance at different resolutions.

• Python 3.7 or later
• OpenCV
• MediaPipe
• NumPy

You can install the required libraries using pip:

pip install opencv-python-headless mediapipe numpy

1. Clone this repository or download the script.
2. Run the script:

python face_landmark.py

• The script includes a loop to test different values for the maximum number of faces detected. The default values tested are 1, 5, 10, 15, 20, and 25. You can modify the max_faces_to_test list to include any other values you want to test.

• To test the minimum face size that the model can detect, you can resize the input frames to various smaller dimensions and observe the detection performance.