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Face Recognition Attendance System

Problem Statement

Traditional student attendance marking technique is often facing a lot of trouble. The face recognition student attendance system emphasizes its simplicity by eliminating classical student attendance marking technique such as calling student names or checking respective identification cards. There are not only disturbing the teaching process but also causes distraction for students during exam sessions. Apart from calling names, attendance sheet is passed around the classroom during the lecture sessions. The lecture class especially the class with a large number of students might find it difficult to have the attendance sheet being passed around the class. Thus, face recognition attendance system is proposed in order to replace the manual signing of the presence of students which are burdensome and causes students get distracted in order to sign for their attendance. Furthermore, the face recognition based automated student attendance system able to overcome the problem of fraudulent approach and lecturers does not have to count the number of students several times to ensure the presence of the students.

Project Flow

  1. Register your account and add your dataset through face detection.
  2. Train the model so that it can learn from the face dataset and store the fundamentals.
  3. Mark your attendance through video capture .
  4. Visual check of your attendance records

Project Set Up

Clone Repository
git clone https://github.com/ayusshh19/Face-recognition-attendance-system.git

Set Up backend

cd backend
cd facerecog
Start server
python manage.py runserver

Set Up Frontend

cd frontend
cd facefront
run frontend
npm start

Output

image

Contributors

Ayush shukla
Janhavi Chaudhari

References

https://towardsdatascience.com/face-detection-on-custom-dataset-with-detectron2-and-pytorch-using-python-23c17e99e162 https://www.analyticsvidhya.com/blog/2022/03/learn-how-to-build-face-detection-system/ https://www.analyticsvidhya.com/blog/2022/04/face-detection-using-the-dlib-face-detector-model/