Face Recognition System

A lightweight, dependency-free face recognition system powered by OpenCV's DNN module.

Table of Contents

• Features
• Requirements
• Quick Start
• Usage
• How It Works
• Configuration
• Contributing
• License

Features

Core Features

✨ Real-time Detection — Identify faces in live video streams
� Multi-Face Support — Detect and recognize multiple people simultaneously
�📸 Image Processing — Analyze faces in static images with annotations
⚡ Zero Setup — Models download automatically on first run
💪 CPU-Only — No GPU required
🔌 Pure OpenCV — No TensorFlow, no compilation, no complications

Enhanced Features

🎨 Beautiful UI — Stylish bounding boxes with corner accents and color coding
📊 Live Dashboard — Real-time statistics panel with session metrics
📸 Screenshot Capture — Save snapshots instantly with S key
🎥 Video Recording — Record recognition sessions with R key
👁️ Face Landmarks — Visual facial feature points overlay
📈 Confidence Meters — Visual confidence bars for each detection
🔔 Unknown Alerts — Get notified when unknown persons are detected
⚙️ Adjustable Settings — Fine-tune detection thresholds on-the-fly
💾 Auto-save Outputs — Annotated images saved to output/ directory

Requirements

Package	Version
Python	3.7+
OpenCV	4.9.0
NumPy	1.26.3

Quick Start

1️⃣ Clone & Setup

git clone https://github.com/yourusername/face-recognition-system.git
cd face-recognition-system
pip install -r requirements.txt

2️⃣ Prepare Training Data

Create a faces/ directory with images of people you want to recognize:

faces/
├── john.jpg
├── sarah.png
└── alex.jpg

Tip: Use filename as the person's name (e.g., john.jpg → recognized as "John")

3️⃣ Run the Application

python main.py

Select your mode:

• Mode 1: Real-time webcam recognition (press q to quit)
• Mode 2: Process a single image file

Usage

Real-time Webcam Recognition (Enhanced Mode)

python main.py
# Select option 1

Keyboard Controls

Key	Action
Q	Quit application
S	Take screenshot
R	Toggle video recording
L	Toggle face landmarks
C	Toggle confidence bars
T	Toggle stats panel
E	Toggle emotion detection
A	Toggle unknown alerts
]	Make recognition MORE strict (fewer false positives)
[	Make recognition LESS strict (more lenient)

Image File Processing

python main.py
# Select option 2
# Provide your image path
# Annotated image saved to output/ directory

View Settings & Statistics

python main.py
# Select option 3 for settings
# Select option 4 for statistics

How It Works

Model Setup (auto-download on first run)
    ↓
Directory Scan (faces/ folder)
    ↓
Face Detection (OpenCV DNN)
    ↓
Face Recognition (LBPH algorithm)
    ↓
Results Display (with confidence scores)

Technical Details

Component	Technology
Face Detection	OpenCV DNN + SSD (Single Shot Detector)
Face Recognition	LBPH (Local Binary Patterns Histograms)
Pre-trained Models	Caffe format (~10MB, auto-downloaded)

Performance Metrics

Metric	Value
Detection Speed	~30 FPS (modern CPU)
Recognition Accuracy	Good for controlled environments
Memory Usage	~100MB (including models)

Configuration

Customize the system by modifying face_recognition.py:

Parameter	Location	Default	Purpose
Detection Confidence	Line 52	0.5	Face detection threshold
Recognition Threshold	Line 137	100	Face match confidence
Face ROI Size	Line 95	(200, 200)	Cropped face dimensions

Contributing

We welcome contributions! Here's how:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Submit a Pull Request

See CONTRIBUTING.md for more details.

License

This project is licensed under the MIT License.

Acknowledgments

• 🖤 OpenCV — For the incredible computer vision library
• 📦 OpenCV model zoo — For pre-trained face detection models

Disclaimer

⚠️ This is an educational face recognition system designed for simple use cases. For production applications requiring high accuracy, consider using advanced models like FaceNet, ArcFace, or commercial solutions.

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