Face Recognition Based Attandance System

Recognizes faces In The Class And Takes Automatic Attandance. 🎇

Motivation 🔥

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The motivation to build a face recognition based attendance system is to improve and enhance the current attendance system. Also, the traditional attendance system takes a lot of time and efforts by the professor which then utilized for the teaching purpose by using this modern approach.

Features 💡

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• Check Camera
• Capture Faces
• Train Images
• Recognize & Attendance
• Auto Mail
• Quit

Screenshots 📷

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Command Line Interface

Checking Camera

Database Preparation

Training

Recognize

Automail

Exit

Tech Used 💻

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Build With -

• Python 3.7

Module Used -

All The Module are Latest Version.

• OpenCV Contrib 4.0.1
• Pillow
• Numpy
• Pandas
• Shutil
• CSV
• Yagmail
• Pickle
• OS
• Time

Face Recognition Algorithms -

• Haar cascade classifier: Database creation
• Face detection: Histogram of oriented gradients (HOG)
• CNN: Creation of 128-d embeddings
• Euclidean distance: Face classification

Software Used -

• Jupyter notebook
• Git

Installation 🔑

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Download or Clone the project

First, download the project from GitHub press Download Zip and open a file in PyCharm or Jupyter notebook. Then we have to create a python environment to run the program.

create enviroment

First open the terminal or command line in the IDE.Then write the following code.

python -m venv env

Then activate the enviroment using the code below for windows.

.\env\Scripts\activate

Installing the packages

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After creating the enviroment on your project let’s install the required packages.

To install those package open the terminal or run the following commands in Jupyter notebook.

pip install opencv-contrib-python

pip install numpy

pip install pandas

pip install Pillow

pip install pytest-shutil

pip install python-csv

pip install yagmail

pip install os

pip install pickle

pip install time

[Notice: During the package installization in Jupyter notebook, add '!' before each package names. For Example: !pip install numpy]

Test Run 🔧

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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

How To Use? 📝

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Follow the steps below:

1. First download the project
2. Import the project to your IDE
3. Create an python enviroment
4. Install all the packages
5. Change the mail information
6. Run the project using the command line or your IDE Run Button

Project Output 💻

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CASE 1

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Case 1- Face recognition in moderate light conditions

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CASE 2

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Case 2- Face recognition in ambient light

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CASE 3

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Case 3- Face recognition in ambient light with strong database

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Efficiency Table

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Certifications 🎓

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Certificate of the paper published!

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Review Paper

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Thesis Paper

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Contribute ❤️

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If you want to contribute in this project feel free to do that.

Credits 💖

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Thanks to [Neha Savakhande], [Parth Shinde], [Kiran Pote], [Dr. Jayant Mahajan sir] for being a part of this project as well as group.