Facial Recognition for Smart Attendance Management System Using Local Binary Patterns Histograms

Abstract

Automatic face recognition (AFR) technologies have made many improvements in the changing world. Smart Attendance using Real-Time Face Recognition is a real-world solution which comes with day-to-day activities of handling student attendance system. In the previous system using QR code which is not easy and intuitive. Students need to get out their phones, open their QR code reader, and scan the code in front of them. The entire process can be long which is frustrating and time consuming. The purpose of this project is to build a smart web-based attendance system which is based on face recognition using OpenCV’s Local Binary Patterns Histograms. The major steps in this system are detecting the faces and recognizing them. After these, the comparison of detected faces can be done by crosschecking with the database of student's faces. The System prototyping is used as the methodology for developing the proposed system. After that, the student attendance record will be saved in the database and the student can see their attendance percentage by logging in the system and lecturer can edit & update if its required. This smart system will be an effective way to maintain the attendance and records of students and the issue of fake attendance and proxies can be solved.