This project implements an advanced attendance system that leverages face recognition technology along with anti-spoofing measures to ensure the authenticity of the attendance process. It's designed to be a comprehensive solution for educational institutions or corporate environments looking to automate their attendance tracking with added security.
These instructions will guide you through setting up and running the AI Attendance System With AntiSpoofing on your local machine.
Before you begin, ensure you have the following installed:
- Python 3.10 (The project is tested and implemented with Python 3.10)
- Conda (Recommended for managing Python versions and virtual environments)
- Clone the repository to your local machine:
git clone https://github.com/CodePeacock/AI-Attendance-and-Anti-Spoofing-System.git- Navigate to the project directory:
cd AI-Attendance-and-Anti-Spoofing-System- Create a virtual environment:
- On Windows (using Conda):
conda create -n sams python=3.10
conda activate ams- On Windows (using venv):
python -m venv venv
venv\Scripts\activate- On Linux or Mac:
python3 -m venv venv
source venv/bin/activate- Install the required dependencies:
pip install -r requirements.txtAfter installing all the dependencies, you can run the project by executing the attendance_w_antispoofing.py script:
python attendance_w_antispoofing.pyThis will launch the AI Attendance System With AntiSpoofing application.
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😊Face Recognition-Based Attendance: Automatically marks attendance by recognizing faces.
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🤖 Anti-Spoofing: Incorporates anti-spoofing measures to prevent fraudulent attendance.
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🏬 Embedding Extraction: Extracts and saves face embeddings for accurate recognition.
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📒 Attendance Records: Maintains detailed attendance records for further analysis. Contributing
Contributions to the AI Attendance System With AntiSpoofing are welcome. Please feel free to fork the repository, make changes, and submit pull requests.
This project is licensed under the MIT License - see the LICENSE file for details.