Smart Attendance System Using QR-Code, Finger Print and Face Recognition

Abstract

Smartphones now play an important role in our daily lives. It has made life simpler and easier for everyone with various social, commercial, problem-solving, educational, and marketing apps... etc., and in light of the need for social distancing following Covid-19, we have designed a system that addresses the problem of student and employee attendance in various institutions. They were utilizing a paper-based approach to keep track of their attendance. The suggested system is an Internet of Things (IoT)-based attendance system that makes use of the IEEE 2413 Standard for an Architectural Framework for the Internet of Things (IoT) (Face recognition -Fingerprint -QR code). Our system may be used in two sorts of organizations: the first is for businesses, where attendance is recorded using one of two characteristics (Face recognition or fingerprint), and the system is also designed to manage employee working hours. The second system is for educational institutions, and it is responsible for managing and analyzing all students' attendance. For pupils to take attendance, a (Face recognition& QR code) will be provided. The professor is in charge of determining whether or not all of the pupils in the group or class are present. To authenticate their attendance and exit, users will only need their smartphones.

Rancang Bangun Aplikasi Kehadiran Siswa Menggunakan Pengenalan Wajah Berbasis Android Dengan Metode Machine Learning

“…Kami mempelajari beberapa penelitian sebelumnya tentang Sistem kehadiran siswa tingkat lanjut menggunakan Pengenalan wajah berbasis Android, adapun penelitian terkait diantaranya yaitu Penelitian Advance Attendance System using Face Recognition and Android [4], Penelitian perancangan Sistem Kehadiran Online menggunakan deteksi wajah dan geofencing dengan menggunakan Deep Learning Berbasis Android [5], Penelitian terkait kinerja deteksi dan pengenalan wajah menggunakan Baidu AI yang menghasilkan, kecepatannya dua kali lebih cepat daripada Face++ pada kondisi pendeteksian secara langsung dengan nilai akurasi pengenalan lebih tinggi dari Face++ hal ini dapat mencegah penipuan gambar secara statis [6], Penelitian Sistem kehadiran berbasis Al dengan pengenalan wajah berbasis komputer dengan kecepatan dan akurasi pengenalan wajah dari kamera pengintai [7], Penelitian terkait perancangan absensi berbasis Internet of Things (IoT) yang menggunakan Standar IEEE 2413 untuk Kerangka Arsitektur untuk Internet of Things (IoT) pada Pengenalan Wajah, Sidik jari dan QR Code [8], Penelitian terkait perancangan sistem kehadiran harian siswa dapat dilacak secara real time dengan pengenalan wajah menggunakan metode pembelajaran mesin untuk menghilangkan kesalahan input secara manual [9].…”