Research on Campus Attendance System Based on Face Recognition and Trajectory Tracking

Abstract

The traditional Morning jog attendance in colleges and universities is limited to manual supervision by swiping card attendance, which is prone to queuing and sign-in on behalf of others. In addition, the lack of quantitative records of exercise data makes supervision difficult. Location-based attendance systems represented by Dingding time attendance, or simple face recognition attendance systems cannot achieve accurate attendance. The campus happy running system based on face recognition and trajectory tracking innovatively combines face recognition and trajectory tracking, and students achieve accurate attendance certification within the designated “geo-fence”. Practice shows that the system can meet the special needs of colleges and universities for Morning jog attendance and data recording, and it effectively improves the accuracy of identity verification.

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

“…Arsitektur dari aplikasi kehadiran siswa menggunakan face recognition [15], [16], [17] dimana dari sisi Back-End: merupakan aplikasi server WSGI [18], [19] yang bertugas sebagai interface dalam menangani penyimpanan dataset wajah user. server menggunakan server environment (.env) dengan library python 3.8, OpenCV Python, Face Recognition, Server Flask, Json dimana user akan melakukan registrasi wajah pada sistem kemudian hasil registrasi akan disimpan pada database wajah sebanyak 51 contoh wajah, jumlah wajah tersebut selanjutnya akan di generate dan dilakukan training data wajah dengan metode KNN [20] (trained_knn_model.clf) untuk menentukan nilai kedekatan dari sampel wajah tersebut dimana nilai kedekatan/akurasi nilai tertentu akan menunjukan akurasi wajah seorang siswa selanjutnya disimpan untuk verifikasi wajah jika ada request dari aplikasi android.…”