🤖 Smart Face Recognition System

A high-performance, real-time facial recognition solution built using Deep Learning and Computer Vision. This system processes live video streams to identify individuals by comparing real-time biometric signatures against a pre-encoded database.

✨ Key Features

• Real-time Recognition: Sub-second identification of individuals via webcam.
• Deep Learning Core: Powered by dlib's state-of-the-art face recognition model (99.38% accuracy).
• Optimized Pipeline: Uses 0.25x frame scaling to maintain high FPS during live processing.
• Multi-Face Support: Capable of detecting and labeling multiple identities simultaneously in a single frame.

🛠️ Tech Stack

• Language: Python
• UI Framework: Streamlit
• Streaming: Streamlit-WebRTC (for local browser-based camera access)
• Computer Vision: OpenCV
• Deep Learning Library: Face-Recognition (dlib)
• Data Handling: NumPy & Pickle

⚙️ Technical Logic

1. Preprocessing: Frames are captured in BGR format, downscaled for speed, and converted to RGB.
2. Feature Extraction: The system detects face locations and generates 128-dimensional encodings.
3. Distance Matching: Live encodings are compared against the encodings.pkl database using a tolerance-based matching algorithm to ensure precise identification.

🚀 Local Setup & Installation

1. Clone the repository:

   git clone [https://github.com/mansiggit/Smart-Face-Recognition-System](https://github.com/mansiggit/Smart-Face-Recognition-System)
   cd smart-face-recognition

2. Install system dependencies (Linux/Ubuntu):

   sudo apt-get update && sudo apt-get install cmake build-essential libgl1-mesa-glx

3. Install Python packages:

   pip install -r requirements.txt

4. Run the App:

   streamlit run app.py
   

📈 Performance

• Latency: <500ms for identification.
• Storage: Minimal footprint using 128-dimensional vector representations.