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All Journal Jurnal Teknik Informatika (JUTIF)
Reiner Anggriawan Jasin
Informatika, Fakultas Teknologi Informasi, Universitas Ciputra Surabaya, Indonesia
Computer Science & IT

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RECOGNITION OF HUMAN FACES IN VIDEO CONFERENCE APPLICATIONS USING THE CNN PIPELINE Evan Tanuwijaya; Reinaldo Lewis Lordianto; Reiner Anggriawan Jasin
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.2.219

Abstract

The COVID-19 pandemic has forced daily face-to-face activities to be carried out online using video conferencing applications. To record participant participation in meetings using a video conference application, an online form application is used. However, participants sometimes do not see this and are often missed due to the large number of incoming chats. Therefore, the use of face detection for attendance using a combination of CNN to detect all the faces in a video conference using YOLO Face and CNN to recognize the owner of a face using Smaller VGG in a pipeline will make it easier to recognize participants who are present at the video conference. The results of the Smaller VGG training are obtained, namely the loss value of 0.059, the accuracy value is 0.995, the recall value is 0.994, the precision value is 0.996. Meanwhile, for the validation phase of the model, the loss value is 0.497, the accuracy value is 0.979, the recall value is 0.979 and the precision value is 0.981. In terms of training duration, the smaller VGG has a duration of 4 minutes and 16 seconds. The Smaller VGG model was combined with YOLO to create a CNN pipeline and was successful in recognizing the faces of video conference participants
Co-Authors Evan Tanuwijaya Reinaldo Lewis Lordianto