A Real-time Face Recognition Smart Attendance System with Haar Cascade Classifiers

Abstract

Taking attendance of students during class time is one of the foremost tasks for teachers and it's pretty much complicated at the same time. The manual attendance system takes an enormous amount of time over the number of students and has a prospect of being a proxy. Day by day it's getting intimidating because the number of students is increasing. A previous couple of year's automated biometric systems like a fingerprint, QR-code technology is using in smart attendance systems. However, time makes the difference here, at present face recognition technology is using to identify the student's participation in the classroom. So, we proposed a smart attendance system to take attendance by detecting and recognizing the face. Our main motivation is to make the attendance system easier, less time-consuming, and also protect from being proxy. The old manual-based attendance system was an arduous process and had a chance of proxy but we will be able to ameliorate the situation. This system can make a crystal clear concept to the machine whether it's a legal attendance or proxy. This system is a more secure and hassle-free system. In this system, all the student's data are recording in the cloud database with time and class schedule. So that teachers can easily evaluate the students in marking on attending. Also, help the teacher to inform individuals' parents whether or not their son/ daughter is irregular in the class. In this system, we used an effective machine learning-based object detection method Haar Cascade classifiers proposed by Paul Viola and Michael Jones for classifying extracting images and recognizing LBPH to this system.