/Automatic-attendance-management-system

ROLLCALL an automatic and smart attendance marking and management system which uses Microsoft Azure’s Cognitive service at its core to create a system that could make sure that no human intervention is required and provides government the ability to monitor the attendance of the schools and helps the government officials in mark fake schools.

Primary LanguagePythonMIT LicenseMIT

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Automatic-attendance-management-system (ROLLCALL)

Hey Coder :👋

Attendance is an important part of our education system. Specially in schools in rural areas where government uses these attendances for their schemes which they introduce to promote education . The daily attendance of the student is used for ordering of mid-day meal by the government. But any malpractice related to marking the attendance of students result in individuals making profit at the cost of future of India. On top of that the food wastage that happens because of this is problematic. There have been numerous reports regarding the malpractice in attendance of the students.

ROLLCALL an automatic and smart attendance marking and management system which uses Microsoft Azure’s Cognitive service at its core to create a system that could make sure that no human intervention is required and provides government the ability to monitor the attendance of the schools and helps the government officials in mark fake schools.

ProjectRollcall

Why this project ?

  • To automate traditional attendance marking process
  • To help government officials keep track of education structure
  • To create a better learning environment and improve education system

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Functionalities

  • Student enrolment
  • Image pre-processing and noise removal
  • Model training
  • Face Detection
  • Database Creation For Attendance
  • Final Report Generation

USP

  • We provide a system which captures attendance of whole class in one click
  • To maintain accuracy we capture faces for 5 times in one hour class without any involvement
  • Using Microsoft Azure services for maintaining accuracy

Hardware used

  1. Raspberry pi
  2. Raspberry pi camera V2
  3. Powerbank ( optional - for electricity issue )

Software used

Raspberry Pi Azure OpenCV SQLite Dlib

Contribution Guidelines

  • Write clear meaningful git commit messages (Do read this).

  • Make sure your PR's description contains GitHub's special keyword references that automatically close the related issue when the PR is merged. (Check this for more info)

  • When you make very very minor changes to a PR of yours (like for example fixing a text in button, minor changes requested by reviewers) make sure you squash your commits afterward so that you don't have an absurd number of commits for a very small fix. (Learn how to squash at here)

  • When you're submitting a PR for a UI-related issue, it would be really awesome if you add a screenshot of your change or a link to a deployment where it can be tested out along with your PR. It makes it very easy for the reviewers and you'll also get reviews quicker.

Project Admin & Mentors 🌟✨


Swarnima Shukla


Bhubesh SR


Sarath Kaul


Suyash Gautam

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