@inproceedings{f6f9f80484d64d75ad03992f19e9323e,
title = "Attendance System on Moving Objects through Face Recognition using MTCNN and CNN",
abstract = "Face detection and recognition using the eigenfaces method accuracy is decreased when there are changes in object distance and lighting levels. The objective of this research is to propose an automatic presence system through face detection using the MTCNN method and facial image recognition using the CNN method. The CNN architecture used in this study is VGG16. Based on the test results, the MTCNN and CNN algorithm can handle the changes in object distance and lighting levels. The face detection system has an average error value of 17%, calculated using MAPE; the error rate is high because other objects cover faces, and some faces use face-covering attributes. The facial recognition system has an average accuracy value of 78% for the first architecture and 87.3% for the second architecture.",
keywords = "attendance system, CNN, face detection, facial image recognition, MTCNN, VGG16",
author = "Bhaskoro, {Susetyo Bagas} and Siti Aminah and Khoutal Taqi",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd International Symposium on Material and Electrical Engineering Conference, ISMEE 2021 ; Conference date: 10-11-2021 Through 11-11-2021",
year = "2021",
doi = "10.1109/ISMEE54273.2021.9774257",
language = "English",
series = "ISMEE 2021 - 2021 3rd International Symposium on Material and Electrical Engineering Conference: Enhancing Research Quality in the Field of Materials and Electrical Engineering for a Better Life",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "184--189",
booktitle = "ISMEE 2021 - 2021 3rd International Symposium on Material and Electrical Engineering Conference",
address = "United States",
}