HUMAN FACE RECOGNITION ON IMAGE VIDEO CONFERENCE APPLICATION USING SIAMESE NETWORK WITH SKIP CONNECTION SMALLER VGG MODEL

Evan Tanuwijaya; Averill Saladin Atma Setiawan; Achmad Rijalu Arianindita; Timothy Kristanto

doi:10.52436/1.jutif.2023.4.5.981

Evan Tanuwijaya Informatics, School of Information Technology, Universitas Ciputra Surabaya, Indonesia
Averill Saladin Atma Setiawan Informatics, School of Information Technology, Universitas Ciputra Surabaya, Indonesia
Achmad Rijalu Arianindita Informatics, School of Information Technology, Universitas Ciputra Surabaya, Indonesia
Timothy Kristanto Informatics, School of Information Technology, Universitas Ciputra Surabaya, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.5.981

Keywords: Convolution Neural Network, Face Recognition, Siamese Network, Skip Connection, VGG, Video Conference Image

Abstract

Attendance recording is needed to find out someone's attendance at a meeting or meeting. These meetings are sometimes conducted online through the video conferencing application. Recording attendance at online meetings is using an online form that is distributed via chat. There are several problems such as chats piling up and meeting participants arriving late so they cannot access the form link. Therefore, facial recognition can be used to record attendance using screenshots as an attendance record with the aim of helping to facilitate attendance recording through video conferencing applications using computer vision technology. This study proposes a method of using the Siamese network with the Smaller VGG skip connection model to improve human face recognition in video conferencing application images. Has validation accuracy results in the training phase of 98%, precision of 98%, and recall of 98%. For the similarity phase where the model is applied to the Siamese network, the accuracy is 95%, the precision is 53%, and the recall is 78%. Then the model is applied to the pipeline system with the YOLO-face model to classify the results of face detection from Yolo with the faces in the database so that the model does not need to be retrained if there are new faces, it only needs to add facial images to the database to be compared with the query image..

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