Face Recognition Tutorial

This repository contains a Python program for real-time face recognition using a webcam. It utilizes the face_recognition library for facial feature detection and comparison, and OpenCV for video processing. Encoded faces are cached in pickle files for faster recognition.

Features

• Load and cache known face encodings from images.
• Perform real-time face detection and recognition from a webcam.
• Display face recognition results with confidence percentages.
• Multi-threaded video capture for improved performance.

Installation

1. Clone the repository:

   git clone https://github.com/jsammarco/face_recog_tutorial.git
   cd face_recog_tutorial

2. Install the required dependencies:

   pip install -r requirements.txt

   Ensure that you have the following installed:

   • Python 3.8 or later
   • face_recognition
   • opencv-python
   • numpy

3. Set up the known_faces directory:

   • Place images of known faces in the known_faces directory.
   • Each image file should be named after the person in the photo (e.g., John_Doe.jpg).

Usage

Run the program to start the face recognition system:

python face_recognition.py

Keyboard Shortcuts

• Press q to quit the program.

Notes

• The program processes every second frame by default for improved performance.
• Face encodings are automatically saved as pickle files (.pkl) in the same directory as the source images.

Directory Structure

face_recog_tutorial/
├── face_recognition.py   # Main program file
├── known_faces/          # Directory for storing known face images
├── requirements.txt      # List of required Python packages

Requirements

• Python 3.8+
• OpenCV
• Face Recognition Library
• Numpy

Troubleshooting

• If the camera does not initialize, ensure it is connected and accessible.
• If the program cannot find faces in the images, check that the images are clear and contain a single face.

License

This project is open source and available under the MIT License.