Face Recognition Using ArcFace and FaceNet in Google Cloud Platform For Attendance System Mobile Application

DOI

10.2991/978-94-6463-106-7_13How to use a DOI?

Keywords

Attendance system; Smartphone; biometrics; facial recognition; CNN

Abstract

The attendance system process in Indonesia generally are still using a traditional method. Paper-based is used as a medium to perform attendance at every event. With this traditional method, there are still many shortcomings in terms of security and management. In terms of security, the traditional attendance system is still quite lacking due to the number of participants cheating by asking their relatives, such as examples of signatures that can still be imitated, or attendance checks can still be tricked because we can change them easily. Therefore, it is necessary to have an attendance system that can be carried out efficiently, safely, and easy to manage, with attendance being done online or using a smartphone. It can be implemented easier for event owners to manage the attendance track of participants, reduce the use of paper, which is quite significant, and secure the attendance system. CNN is an artificial neural network that is more often used in visual image analysis. CNN can distinguish visual images from one another with various aspects given. The models that we used for this application are ArcFace and FaceNet. Three different BackEnd Encoder and BackEnd recognized are used, RetinaFace, MTCNN, and OpenCV. From the experiment, we suggest the usage of ArcFace in RetinaFace for High Accuracy of recognition but with high-cost drawbacks, the longer computation time for encoding and recognition. As an alternative, ArcFace with MTCNN can be used with faster computation time but less accurately than RetinaFace.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rosa Andrie Asmara
AU  - Brian Sayudha
AU  - Mustika Mentari
AU  - Rizky Putra Pradana Budiman
AU  - Anik Nur Handayani
AU  - Muhammad Ridwan
AU  - Putra Prima Arhandi
PY  - 2022
DA  - 2022/12/29
TI  - Face Recognition Using ArcFace and FaceNet in Google Cloud Platform For Attendance System Mobile Application
BT  - Proceedings of the 2022 Annual Technology, Applied Science and Engineering Conference (ATASEC 2022)
PB  - Atlantis Press
SP  - 134
EP  - 144
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-106-7_13
DO  - 10.2991/978-94-6463-106-7_13
ID  - Asmara2022
ER  -