RANCANGAN PROTOTYPE SISTEM PENGENALAN WAJAH SECARA REALTIME MENGGUNAKAN FACENET DAN MULTI-TASK CASCADED CONVOLUTIONAL NETWORKS (MTCNN) UNTUK MENDETEKSI KECURANGAN

Gunawan, Luswis Arya (2024) RANCANGAN PROTOTYPE SISTEM PENGENALAN WAJAH SECARA REALTIME MENGGUNAKAN FACENET DAN MULTI-TASK CASCADED CONVOLUTIONAL NETWORKS (MTCNN) UNTUK MENDETEKSI KECURANGAN. Skripsi thesis, IIB Darmajaya.

[img] Text
Cover.pdf
Download (29kB)
[img] Text
Abstrak.pdf
Download (8kB)
[img] Text
Halaman Pengesahan.pdf
Download (272kB)
[img] Text
Daftar Isi.pdf
Download (70kB)
[img] Text
Bab 1.pdf
Download (15kB)
[img] Text
Bab 2.pdf
Download (678kB)
[img] Text
Bab 3.pdf
Download (48kB)
[img] Text
Bab 4.pdf
Download (201kB)
[img] Text
Bab 5.pdf
Download (6kB)
[img] Text
Daftar Pustaka.pdf
Download (74kB)
[img] Text
Lampiran.pdf
Download (133kB)

Abstract

Facial recognition is a face-oriented recognition method. The recognition of human facial imagesis one of the technologies advancing in the field of computer vision, with applications in biometric recognition systems, searching and indexing in digital video databases, security access control, video conferencing, and human-computer interaction. The Multi-Task Cascaded Convolutional Networks (MTCNN) algorithm is one of the algorithms used to detect a face. The proposed facialrecognition utilizesfacial objects with varying positions captured from a webcamconnected to a computer or using a built-in laptop webcam. In this study, we extend the application of facial recognition technology to the online quiz environment. The objective is to implement a real-time web-based facial recognition system that leverages the power of FaceNet and MTCNN to detect potential fraudulent activities during online quizzes. By integrating this technologies, the aim is to enhance the security and integrity of online assessments by continuously monitoring participants' facial expressions and activities. This innovative approach will help identify and mitigate potential instances of cheating, ensuring a fair and trustworthy online examination experience. The research results in a system that can detect indications of cheating during online quizzes. Keywords: face recognition, MTCNN, computer vision, FaceNet, CNN

Item Type: Thesis (Skripsi)
Subjects: Ilmu Komputer
eSkripsi
Divisions: Skripsi/TA & PKPM/KP - Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: Luswis Arya Gunawan
Date Deposited: 07 May 2024 06:36
Last Modified: 07 May 2024 06:36
URI: http://repo.darmajaya.ac.id/id/eprint/15901

Actions (login required)

View Item View Item