Face Recognition and Face Spoofing Detector for Attendance System

• Main Author: Marutotamtama, Jane Chrestella
• Other Authors: Setyawan, Iwan, Handoko, Handoko
• Format: Thesis
• Language: English
• Published: 2022
• Subjects: Attendance system; Face recognition; Video spoofing; Deep metric learning; DML
• Online Access: https://repository.uksw.edu//handle/123456789/28336

Improving technology in the field of education will greatly help teachers and students in various ways, one of which is attendance. An attendance system generally leaks security and verification which may lead to fraud activity. In this paper, we design an attendance system that utilizes multiple verifications using card tapping and face recognition which is also accompanied by an anti-video spoofing system. The designed system is implemented as a web application with various algorithms such as Convolutional Neural Network (CNN), and Deep Metric Learning (DML) along with the application of the PN532 sensor and the use of ESP8266 for tapping the card. Our experiments show that the proposed system performs well, achieving up to 87.50% system accuracy.