face-recognition-system

The purpose of the attendance monitoring system using face recognition is to ease the attendance process which consumes lot of time and efforts; it is a convenient and easy way for students and teacher. The system will capture the images of the students and using face recognition algorithm mark the attendance in the sheet.

https://github.com/palashhawee/face-recognition-system

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.5%) to scientific vocabulary

Keywords

attendance-management-system face-recognition facerecognitionproject machine-learning python student-management
Last synced: 6 months ago · JSON representation ·

Repository

The purpose of the attendance monitoring system using face recognition is to ease the attendance process which consumes lot of time and efforts; it is a convenient and easy way for students and teacher. The system will capture the images of the students and using face recognition algorithm mark the attendance in the sheet.

Basic Info
  • Host: GitHub
  • Owner: PalashHawee
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 86.2 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 1
  • Open Issues: 1
  • Releases: 1
Topics
attendance-management-system face-recognition facerecognitionproject machine-learning python student-management
Created almost 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme License Citation

README (1).md

Face Recognition Attendance System

Abstract

In the era of modern technologies emerging at rapid pace there is no reason why a crucial event in educational sector such as attendance should be done in the old boring traditional way. Attendance monitoring system will save a lot of time and energy for the both parties students as well as the class teachers. Attendance will be monitored by the face recognition algorithm by recognizing only the face of the students from the rest of the objects and then marking them as present. The system will be pre feed with the images of all the students and with the help of this pre feed data the algorithm will detect them who are present and match the features with the already saved images of them present in the database

Acknowledgements

I express my deep sense of gratitude towards Assistant Prof. Dr. Somshekhar MT for his valuable guidance and his interest; I am able to complete this project in scheduled time. I am indebted to our honorable principal Prof. Dr. Hanumanthappa who has been a constant source of motivation and co-operating in bringing this project in very short time Lastly, I am thankful to all other staff members of MCA Department from Bangalore University who have directly and indirectly helped me while completing this project report.

Introduction

The purpose of the attendance monitoring system using face recognition is to ease the attendance process which consumes lot of time and efforts; it is a convenient and easy way for students and teacher. The system will capture the images of the students and using face recognition algorithm mark the attendance in the sheet. This way the class-teacher will get their attendance marked without actually spending time in traditional attendance marking. The identification process to determine the presence of a person in a room or building is currently one of the routine security activities. Every person who will enter a room or building must go through several authentication processes first, that later these information’s can be used to monitor every single activity in the room for a security purpose. Authentication process that is being used to identify the presence of a person in a room or building still vary. The process varies from writing a name and signatures in the attendance list, using an identity card, or using biometric methods authentication as fingerprint or face scanner.

Installation

Install my-project with npm

```bash see requirements.txt

``` SOFTWARE REQUIREMENTS PLATFORM

Operating system: Windows OS
Platform: Android Studio
Programming language: Python

HARDWARE REQUIREMENTS

Processor: INTEL Pentium 4 Processor Core
Hard Disk: 40 GB (min)
Ram: 256 MB or higher

System Design

Algorithm used:

Flow Chart

Flow Chart

ER Diagram

ER Diagram

Features

  • Adding Student Details
  • Deleting Student Details
  • Editing Student Details
  • Take the Student Photo Sample
  • Import Attendace in CSV format
  • Export Attendace in CSV format
  • ChatBot
  • Contact with Developer
  • Automatic Attendace with Face Recognition

Database

Relational Database Management System (RDBMS)

Language

Python

Login Page

Log In

Registration Page

Registration

Home Page

Home

Owner

  • Name: Palash Hawee
  • Login: PalashHawee
  • Kind: user
  • Location: Dhaka, Bangladesh

Aspiring Software Engineer

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Hawee"
  given-names: "Palash"
  orcid: "https://orcid.org/my-orcid?orcid=0000-0002-1041-1474"
title: "Face-Recognition-System"
version: 1.0.0
doi: 10.5281/zenodo.6477050
date-released: 2022-04-22
url: "https://doi.org/10.5281/zenodo.6477050"

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Last synced: 11 months ago

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  • VivekAllamsetty30 (1)
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Dependencies

requirements.txt pypi
  • Django ==4.0.2
  • Flask ==1.1.2
  • GitPython ==3.1.26
  • Jinja2 ==2.11.3
  • Kivy ==2.0.0
  • Kivy-Garden ==0.1.4
  • MarkupSafe ==1.1.1
  • Pillow ==8.1.1
  • PyMySQL ==1.0.2
  • PyPDF2 ==1.26.0
  • PyYAML ==5.4.1
  • Pygments ==2.8.0
  • Pympler ==1.0.1
  • Send2Trash ==1.8.0
  • Werkzeug ==1.0.1
  • altair ==4.2.0
  • altgraph ==0.17
  • appdirs ==1.4.4
  • argon2-cffi ==21.3.0
  • argon2-cffi-bindings ==21.2.0
  • asgiref ==3.5.0
  • astor ==0.8.1
  • astroid ==2.4.2
  • asttokens ==2.0.5
  • attrs ==21.4.0
  • autopep8 ==1.5.5
  • backcall ==0.2.0
  • base58 ==2.1.1
  • beautifulsoup4 ==4.9.3
  • black ==22.1.0
  • bleach ==4.1.0
  • blinker ==1.4
  • cachetools ==5.0.0
  • certifi ==2020.12.5
  • cffi ==1.15.0
  • cfgv ==3.2.0
  • chardet ==4.0.0
  • click ==8.0.3
  • colorama ==0.4.4
  • cssselect ==1.1.0
  • csvwriter ==0.2.2
  • cycler ==0.10.0
  • debugpy ==1.5.1
  • decorator ==5.1.1
  • defusedxml ==0.7.1
  • distlib ==0.3.1
  • docutils ==0.16
  • entrypoints ==0.4
  • executing ==0.8.2
  • feedfinder2 ==0.0.4
  • feedparser ==6.0.2
  • filelock ==3.0.12
  • future ==0.18.2
  • gTTS ==2.2.2
  • gitdb ==4.0.9
  • h5py ==3.2.1
  • identify ==1.6.1
  • idna ==2.10
  • imageai ==2.1.5
  • ipykernel ==6.9.1
  • ipython ==8.0.1
  • ipython-genutils ==0.2.0
  • ipywidgets ==7.6.5
  • isort ==5.7.0
  • itsdangerous ==1.1.0
  • jedi ==0.18.1
  • jieba3k ==0.35.1
  • joblib ==1.0.1
  • jsonschema ==4.4.0
  • jupyter-client ==7.1.2
  • jupyter-core ==4.9.2
  • jupyterlab-pygments ==0.1.2
  • jupyterlab-widgets ==1.0.2
  • kivy-deps.angle ==0.3.0
  • kivy-deps.glew ==0.3.0
  • kivy-deps.sdl2 ==0.3.1
  • kiwisolver ==1.3.1
  • lazy-object-proxy ==1.4.3
  • lxml ==4.6.2
  • matplotlib ==3.3.4
  • matplotlib-inline ==0.1.3
  • mccabe ==0.6.1
  • mistune ==0.8.4
  • mypy-extensions ==0.4.3
  • mysql ==0.0.3
  • mysql-connector ==2.2.9
  • mysql-connector-python ==8.0.27
  • mysqlclient ==2.1.0
  • nbclient ==0.5.11
  • nbconvert ==6.4.2
  • nbformat ==5.1.3
  • nest-asyncio ==1.5.4
  • newspaper3k ==0.2.8
  • nltk ==3.5
  • nodeenv ==1.5.0
  • nose ==1.3.7
  • notebook ==6.4.8
  • numpy ==1.20.1
  • opencv-contrib-python ==4.5.5.62
  • opencv-python ==4.5.5.62
  • packaging ==20.9
  • pandas ==1.2.2
  • pandocfilters ==1.5.0
  • parso ==0.8.3
  • pathspec ==0.9.0
  • pefile ==2019.4.18
  • pickleshare ==0.7.5
  • platformdirs ==2.5.0
  • pluggy ==0.13.1
  • pre-commit ==2.10.1
  • prometheus-client ==0.13.1
  • prompt-toolkit ==3.0.28
  • protobuf ==3.19.1
  • pure-eval ==0.2.2
  • py ==1.10.0
  • pyarrow ==7.0.0
  • pycodestyle ==2.6.0
  • pycparser ==2.21
  • pydeck ==0.7.1
  • pyinstaller ==4.2
  • pyinstaller-hooks-contrib ==2020.11
  • pyjokes ==0.6.0
  • pylint ==2.6.2
  • pymongo ==3.11.4
  • pyparsing ==2.4.7
  • pypiwin32 ==223
  • pyrsistent ==0.18.1
  • python-dateutil ==2.8.1
  • pytz ==2021.1
  • pytz-deprecation-shim ==0.1.0.post0
  • pywin32 ==300
  • pywin32-ctypes ==0.2.0
  • pywinpty ==2.0.2
  • pyzmq ==22.3.0
  • regex ==2020.11.13
  • requests ==2.25.1
  • requests-file ==1.5.1
  • scikit-learn ==1.0.2
  • scipy ==1.6.2
  • seaborn ==0.11.2
  • selenium ==3.141.0
  • sgmllib3k ==1.0.0
  • six ==1.15.0
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  • soupsieve ==2.2
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  • terminado ==0.13.1
  • testpath ==0.5.0
  • threadpoolctl ==3.0.0
  • tinysegmenter ==0.3
  • tldextract ==3.1.0
  • toml ==0.10.2
  • tomli ==2.0.1
  • toolz ==0.11.2
  • tornado ==6.1
  • tox ==3.22.0
  • tqdm ==4.58.0
  • traitlets ==5.1.1
  • translate ==3.5.0
  • typing-extensions ==4.1.1
  • tzdata ==2021.5
  • tzlocal ==4.1
  • urllib3 ==1.26.3
  • validators ==0.18.2
  • virtualenv ==20.4.2
  • watchdog ==2.1.6
  • wcwidth ==0.2.5
  • webencodings ==0.5.1
  • widgetsnbextension ==3.5.2
  • wrapt ==1.12.1
  • xlrd ==2.0.1
  • xmltodict ==0.12.0