Attendance by Face Recognition

This project is a face recognition-based attendance system that uses Python, OpenCV, Scikit-learn, Streamlit, and various other libraries like Pandas, Numpy, Datetime, and OS for different functionalities. It enables adding faces to the database, taking attendance based on face recognition, and showing live attendance through a web interface built using Streamlit.

Table of Contents

• Features
• Technologies Used
• Project Setup

Features

• Add Faces: Allows users to add new faces to the database for recognition.
• Take Attendance: Uses face recognition to mark attendance for individuals.
• Live Attendance: Displays live attendance on a web page through Streamlit.
• Data Storage: Keeps track of recognized faces and their attendance records using Pandas.
• Web Interface: A user-friendly interface to manage and view attendance in real-time.

Technologies Used

• Python: Core programming language.
• OpenCV: For capturing and processing image data and face recognition.
• Scikit-learn: Used for face recognition model training.
• Streamlit: Used for building the web interface to show live attendance.
• Pandas: For data manipulation and handling attendance records.
• Numpy: For numerical operations.
• Datetime: For timestamping attendance records.
• OS: For file system operations.

Project Setup

Prerequisites

• Python 3.x installed
• Install the required Python libraries by running the following command:

pip install -r requirements.txt

###Required Libraries

• OpenCV: For image and video processing.
• Scikit-learn: For face recognition model training.
• Streamlit: For building the web interface.
• Pandas: For data handling and manipulation.
• Numpy: For numerical operations.
• Datetime: For managing date and time-based attendance records.