Facial Recognition System with OpenCV and SQLite

This project implements a facial recognition system using OpenCV and SQLite. It includes three main components: face registration, model training, and face recognition. The system captures face images, stores user information in a SQLite database, trains a face recognizer, and then uses the trained model to recognize faces in real-time.

Project Components

1. register.py:

   • Captures face images from the webcam and saves them to the dataSet directory.
   • Stores user information in an SQLite database (test.db).
   • Ensures that each user has multiple face images for better training.

2. trainner.py:

   • Reads face images from the dataSet directory.
   • Trains the LBPH (Local Binary Patterns Histogram) face recognizer.
   • Saves the trained model to trainer/trainer.yml.

3. facerec.py:

   • Loads the trained face recognizer model.
   • Performs real-time face recognition using the webcam.
   • Displays the recognized user's ID and name on the video feed.

Setup Instructions

1. Install Dependencies: Install the required Python packages using pip:

   pip install -r requirements.txt

2. Download Haar Cascade: Make sure you have the Haar Cascade XML file (haarcascade_frontalface_default.xml). You can download it from the OpenCV GitHub repository.

3. Run Registration: Execute register.py to capture face images and store user information:

   python register.py

4. Train the Model: Run trainner.py to train the face recognizer model:

   python trainner.py

5. Run Face Recognition: Start facerec.py to perform real-time face recognition:

   python facerec.py
   

Troubleshooting

• No Faces Detected: Ensure the face images are clear and properly lit. Adjust the detectMultiScale parameters in trainner.py and facerec.py if necessary.
• Model Not Saving: Verify that the trainer directory exists and has write permissions.

@Copyright 2020 | Veendy