🔍 Face Recognition System using Python

A real-time face recognition system that identifies and labels known individuals from a live webcam feed. Built using Python's face_recognition library powered by dlib's deep learning model.

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📸 Demo

[Live Webcam Feed]
  → Known faces loaded from image dataset
  → Each frame scanned for faces
  → Matched face labeled with name
  → Unknown faces labeled as "Unknown"

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⚙️ How It Works

1. Encoding Known Faces — Loads reference images and computes 128-dimension face encodings
2. Live Feed Capture — Streams webcam frames in real-time
3. Face Detection — Detects face locations in each frame
4. Encoding Comparison — Compares live face encodings against known encodings using Euclidean distance
5. Label Display — Draws a bounding box and name label for each matched face

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🛠️ Tech Stack

Tool	Purpose
Python 3.x	Core programming language
face_recognition	Deep learning-based face encoding & matching
OpenCV	Real-time video capture and display
dlib	Underlying facial landmark detection

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📦 Installation

# Clone the repository
git clone https://github.com/arunkumararavindhakshan05-sudo/Face_recognition.git
cd Face_recognition

# Install dependencies
pip install face_recognition opencv-python

⚠️ Note: face_recognition requires dlib which needs CMake. On Windows, install Visual Studio Build Tools before running pip install.

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▶️ Usage

1. Add reference images to the known_faces/ folder, named as PersonName.jpg
2. Run the script:

python face_recognition_live.py

Controls:

• Press Q — Quit the camera feed

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📁 Project Structure

Face_recognition/
│
├── known_faces/              # Folder with reference images (Name.jpg)
├── face_recognition_live.py  # Main script
└── README.md

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🔮 Future Improvements

• Add a database to store and manage known faces
• Log recognition events with timestamps
• Build a web interface using Flask
• Add anti-spoofing (liveness detection)

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👤 Author

Arunkumar Aravindhakshan 🔗 LinkedIn | GitHub