Face Recognition System using OpenCV & LBPH

A real-time face recognition application built with Python, OpenCV, and Tkinter.

This project demonstrates a complete computer vision pipeline — from capturing face images using a webcam to training a machine learning model and recognizing people in real time with confidence scores.

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👨‍💻 Author

Harish Kumar

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🛠 Technologies Used

• Python
• OpenCV
• LBPH Face Recognizer
• Haar Cascade Classifier
• Tkinter (GUI)
• NumPy
• Pillow

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📖 Project Overview

The system works in three major stages:

1. Dataset Generation
   The user enters their name and the webcam captures multiple face images.
   These images are stored locally to create a training dataset.

2. Model Training
   The captured images are processed and used to train an LBPH classifier.

3. Face Recognition
   The trained model is used to detect and recognize faces from the webcam in real time, displaying the predicted name and confidence score.

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⚙️ How It Works

• Haar Cascade detects faces from the camera feed
• LBPH extracts features and compares them with trained faces
• Tkinter provides a simple graphical interface
• OpenCV handles image processing and camera access

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▶️ How to Run

Follow these steps carefully to run the Face Recognition System on your machine.

Step 1: Install Python

Make sure Python 3.8 or higher is installed.

Check version:

python --version

Step 2: Clone the Repository

git clone https://github.com/Harishyadav44/Face_Recognition_GUI.git cd Face_Recognition_GUI

Step 3: Install Required Libraries

pip install -r requirements.txt

Step 4: Run the Application

python main.py

Step 5: Use the Application

1. Click Generate Dataset

Enter your name and look at the camera. The system will capture around 50 face images.

2. Click Train Model

This will train the LBPH face recognition model.

3. Click Detect Face

The webcam will open and display the recognized name with confidence.

Step 6: Exit

    Press Enter on the keyboard to close the webcam window.

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🖥 Application Buttons

Generate Dataset → Captures face images for a new user

Train Model → Trains the LBPH face recognizer

Detect Face → Starts real-time recognition using the webcam

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🔒 Privacy Note

Dataset images and trained model files are not included in this repository for privacy reasons. You can generate your own dataset using the “Generate Dataset” button.

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🚀 Future Improvements

Attendance system integration

Face mask detection

Database connectivity

Web-based interface