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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Sunario Megawan
Universitas Mikroskil, Medan
Computer Science & IT Decision Sciences, Operations Research & Management

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Deteksi Non-Spoofing Wajah pada Video secara Real Time Menggunakan Faster R-CNN Sunario Megawan; Wulan Sri Lestari; Apriyanto Halim
Journal of Information System Research (JOSH) Vol 3 No 3 (2022): April 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.976 KB) | DOI: 10.47065/josh.v3i3.1519

Abstract

Face non-spoofing detection is an important job used to ensure authentication security by performing an analysis of the captured faces. Face spoofing is the process of fake faces by other people to gain illegal access to the biometric system which can be done by displaying videos or images of someone's face on the monitor screen or using printed images. There are various forms of attacks that can be carried out on the face authentication system in the form of face sketches, face photos, face videos and 3D face masks. Such attacks can occur because photos and videos of faces from users of the facial authentication system are very easy to obtain via the internet or cameras. To solve this problem, in this research proposes a non-spoofing face detection model on video using Faster R-CNN. The results obtained in this study are the Faster R-CNN model that can detect non-spoof and spoof face in real time using the Raspberry Pi as a camera with a frame rate of 1 fps.
Co-Authors Apriyanto Halim Wulan Sri Lestari