README.md

🎯 Smart Attendance System using Face Detection with OpenCV

👨‍💻 Software Engineering Mini Project

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📚 Libraries Used:

• NumPy – Powerful Python library for numerical computing, offering support for multi-dimensional arrays and a wide range of mathematical operations.
• Pandas – A fast, flexible, and expressive tool for data manipulation and analysis built on top of Python.
• Haar Cascade Classifier – A machine learning object detection algorithm used to identify faces in images and videos. Based on the work of Paul Viola and Michael Jones (2001).
• face_recognition – The world’s simplest library for face recognition in Python, allowing easy face detection and manipulation.
• OpenCV – A highly optimized open-source library for real-time computer vision and image processing.

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🚀 How to Run the Project:

🔧 Prerequisites:

1. Python 3.x installed
2. A code editor or IDE (e.g., VS Code, PyCharm)

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🛠️ Setup Instructions:

1. Clone or download the project from this repository.
2. Open the project in your preferred IDE.
3. Create and activate a Python virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

4. Install all required libraries:

   pip install -r requirements.txt

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▶️ Run the Application:

1. Open your terminal in the project directory.
2. Install Streamlit (if not already installed):

   pip install streamlit

3. Launch the app:

   streamlit run app.py

   ✅ This will automatically open a new tab in your default web browser where the application UI will start running.

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🧠 Project Highlights:

• Real-time face detection using webcam
• Automatic attendance marking based on face recognition
• Easy-to-use and lightweight interface
• Scalable to multiple users/classes

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📌 Note:

Ensure your webcam is enabled and working before running the application.

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