This project showcases a robust facial recognition system developed using OpenCV and the LBPH (Local Binary Patterns Histograms) algorithm. The system aims to identify and verify individuals by analyzing unique facial features.  

The project follows a three-step approach:

1. Data Collection: Using a webcam, the system creates a dataset of faces, assigning each person a unique ID (integer) for identification.

2. Training the Recognizer: The collected data is used to train the LBPH recognizer, which generates a YML file containing the trained data.

3. Facial Recognition: With the trained data and YML file, the system can accurately recognize and label individuals in real-time.

This facial recognition project demonstrates the power of computer vision and machine learning techniques to build an efficient and reliable biometric authentication system.