Face Recognition based Attendance System using Machine Learning
In the near future, face recognition-based attendance systems will be utilized in all places instead of the traditional approach; it may even replace biometric attendance systems. The goal of this project is to create a new attendance system based on real-time facial recognition. Face recognition technology is also used by Facebook, which tags the names of faces when you submit photographs that have already been tagged by you. The proposed system recognizes and encodes the unique traits of the faces in the database into a pattern image. Python modules are employed in our suggested system, and a machine learning approach called a classifier is used to discover the person’s name. The steps of the operation are image capture, facial features, face recognition, and an attendance system. This model combines a camera that records an input image, an algorithm for detecting faces in input images, encoding and identifying the faces, and recording attendance in a spreadsheet. The training database is built by inputting the faces of permitted pupils into the system. After that, the cropped photographs are saved in a database with their associated labels. The attendance is recorded using the matching face.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask