Automatic Attendance Update System using
Image Processing [2017]

Overview of the Project.

The image of the class is captured via the mobile app.



The Recognition algorithm successfully found the students in the image, and this information is ready to be submitted. 

Key features are extracted using the BSIF, and the students are recognized. 

Once the Send button is pressed on the mobile application, the attendance information is sent to the website, and the attendance is updated.

This project is a real-time solution that streamlines attendance marking in classrooms by utilizing face detection and recognition technology. Instead of traditional roll-call methods, this system captures images of students, processes them using the Viola-Jones algorithm for face detection, and applies machine learning techniques like Binarized Statistical Image Features (BSIF) for recognition. Once the faces are identified, attendance is automatically marked in a MySQL database. The system integrates with both a web application for viewing and managing attendance records and an Android application for capturing and sending images to a server for processing. The project eliminates the need for manual attendance, saves time, and reduces the possibility of proxy attendance. It allows instructors to take attendance with just a few clicks, ensuring a faster and more accurate process.