👁️ OpenCV Python Facial Detection & Recognition (Web – Flask) This project is a lightweight, real-time face detection and recognition system built with Python, OpenCV, and Flask. It uses webcam input and compares detected faces with saved facial data for instant recognition — no deep learning models or heavy training involved.

Currently, the project runs in a web browser, with planned support for Android in future releases.

✨ Features

• 🎯 Real-time face detection using OpenCV

• 👤 Lightweight facial recognition using saved face data (no deep learning)

• 💡 Fast and efficient: no heavy model training

• 📸 Add new faces dynamically to the dataset

• 🖥️ Simple, clean Flask-based web interface

• 📱 Android support planned (via mobile client or REST API)

⚙️ Technologies Used

• Python 3

• Flask

• OpenCV (cv2)

• NumPy

• sklearn (KNN method)

• HTML5

🚀 Getting Started

🔧 Prerequisites

-Python 3.x

-pip

-A webcam

-virtualenv (optional)

📦 Installation

1. Clone the repo

git clone https://github.com/your-username/opencv-face-recognition-flask.git
cd opencv-face-recognition-flask

2. (Optional) Create virtual environment

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install dependencies

pip install flask flask-cors scikit-learn pillow opencv-python-headless numpy

🧠 How It Works

📸 Face detection is performed using OpenCV Haar cascades.

🧬 When a new face is registered, the facial encoding and name are extracted and saved in a .pkl file (using Python’s pickle module).

📂 This .pkl file stores all known users' face data (encodings) and their corresponding names — acting as the facial database.

🧠 A KNN classifier (from scikit-learn) is optionally used to recognize users based on this stored data.

🔍 During runtime, incoming face encodings from webcam input are compared with the ones in the .pkl file for instant recognition.

✅ No deep learning or cloud processing involved — the system is lightweight, fully local, and fast.

📁 Project Structure

opencv-face-recognition-flask/
├── static/             # CSS/JS/Images
├── templates/          # HTML files
├── data/               # Saved facial images Web
│   └── haarcascade_frontalface_default.xml   # Haar cascade classifier
├── android/            # Saved facial data (Android - future support)
└── test.py              # Flask main app

📽️ Video Demo

📱 Android Support (Coming Soon) We're planning to add Android integration via a mobile client or using API endpoints to allow mobile face detection and recognition using the same system.