IJMLC 2019 Vol.9(5): 668-674 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.856

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Tata Sutabri, Pamungkur, Ade Kurniawan, and Raymond Erz Saragih

Abstract—Student attendance is essential in the learning process. To record student attendance, several ways can be done; one of them is through student signatures. The process has several shortcomings, such as requiring a long time to make attendance; the attendance paper is lost, the administration must enter attendance data one by one into the computer. To overcome this, the paper proposed a web-based student attendance system that uses face recognition. In the proposed system, Convolutional Neural Network (CNN) is used to detect faces in images, deep metric learning is used to produce facial embedding, and K-NN is used to classify student's faces. Thus, the computer can recognize faces. From the experiments conducted, the system was able to recognize the faces of students who did attend and their attendance data was automatically saved. Thus, the university administration is alleviated in recording attendance data.

Index Terms—Student attendance system, convolutional neural network, deep metric learning, K-nearest neighbor.

Tata Sutabri is with the Department of Information Systems, Faculty Information Technology, Universitas Respati Indonesia (e-mail: tata.sutabri@gmail.com).
Pamungkur is with the Department of Economic Development, Sekolah Tinggi Ilmu Ekonomi Kuala Kapuas, Central Kalimantan, Indonesia (e-mail: pamungkur@yahoo.com).
Ade Kurniawan and Raymond Erz Saragih are with the Department of Informatics Engineering, Universal University, Batam, Kepulauan Riau, Indonesia (e-mail: ade.kurniawan@uvers.ac.id, raymonde.saragih@gmail.com).

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Cite: Tata Sutabri, Pamungkur, Ade Kurniawan, and Raymond Erz Saragih, "Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 668-674, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net