Face Recognition Based Attendance System using Python and ML
Introduction
In today’s fast-paced world, traditional attendance systems are becoming outdated and inefficient. Companies and institutions are now turning to more advanced technologies, such as face recognition, to streamline their attendance tracking processes. Face recognition technology has revolutionized the way we interact with computers and machines. By using biometric data, such as facial features, we can easily identify and authenticate individuals. This technology has found its way into various applications, including attendance systems.
Table of Contents
Step 1: Making the Project
To create a face recognition-based attendance system, we first need to gather a dataset of facial images. This dataset will be used to train our machine learning model to recognize different faces. We can use libraries such as OpenCV and Dlib in Python to capture and process facial images.
To create an environment and install the required packages for running the project, follow these steps:
Open the terminal or command line within your IDE (PyCharm or VS Code).
Create a new Python virtual environment by executing the following command:
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python -m venv env
Activate the newly created environment. For Windows, use the following command:
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.\env\Scripts\activate
Now, your environment is activated, and you should see (env) in your command prompt to indicate that. This means you are now working within your virtual environment.
After activating the environment, you can install the required packages. Usually, you would have a requirements.txt file containing the list of packages and their versions. You can install them using pip with the following command:
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pip install -r requirements.txt
Replace requirements.txt with the actual name of your requirements file.
Once all the packages are installed, you’re ready to run the program. You can execute your Python scripts within this activated environment, ensuring that they have access to the installed packages without conflicts.
After creating the environment on your project let’s install the necessary packages.
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pip install opencv-contrib-python
Face Recognition Based Attendance System using Python and ML
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pip install numpy
Face Recognition Based Attendance System using Python and ML
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pip install pandas
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pip install Pillow
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pip install pytest-shutil
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pip install python-csv
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pip install yagmail
After creating the enviroment and installing the packages, open the IDE terminal/command line to run the program. Using the code below.
py main.py
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Change the mail information
Run the project using the command line or your IDE Run Button
Step 5: Screenshots
Step 6 : Buy and Download [ Note: Project Only for Educational Purpose]
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FOR YOUR OWN SAFETY, PLEASE:
1. Re-scan downloaded files using your personal virus checker before using it.
2. NEVER, EVER run compiled files (.exe’s, .ocx’s, .dll’s etc.)–only run source code
This document file contains a project Synopsis, Reports, and various diagrams. Also, the abstract pdf file is inside the zip and you can modify it accordingly. Documents and Reports take 2-3 days to create as per the user Requirements
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Conclusion
In conclusion, the integration of face recognition technology and machine learning algorithms in attendance tracking systems offers a more efficient and accurate solution. Implementing essential features, selecting the required software and tools, and developing a user-friendly web application are vital steps in creating a successful face recognition-based attendance system. Stay ahead of the curve by embracing the advancements in technology and optimizing attendance management processes with this innovative solution.
Keywords
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