Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain

Face anti-spoofing is a revolutionary technology involved in various aspects of daily life. Specifically, facial anti-spoofing is a detection process that involves using printed or even keepsakes to mimic genuine facial appearances, and it is related to the facial detection application. The problems...

Full description

Saved in:

Bibliographic Details
Main Author:	Bahrain, Siti Nurul Izzah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96593/1/96593.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.96593
record_format	uketd_dc
spelling	my-uitm-ir.965932024-06-09T04:18:42Z Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain 2024 Bahrain, Siti Nurul Izzah Neural networks (Computer science) Face anti-spoofing is a revolutionary technology involved in various aspects of daily life. Specifically, facial anti-spoofing is a detection process that involves using printed or even keepsakes to mimic genuine facial appearances, and it is related to the facial detection application. The problems that face anti-spoofing are the need for security enhancement, the lack of biometric authentication, and the system's vulnerabilities in manipulating facial detection. In this project, the Convolutional Neural Network (CNN) algorithm was implemented using TensorFlow in Python to detect fake face images. The model facilitated a straightforward construction of the CNN, allowing for sequential handling of inputs. The model included Conv2D and MaxPooling2D layers for feature extraction, followed by a flattened layer and a dense layer with dense, dropout, and batch normalization layers. This project is due to its ability to do face detection and anti-spoofing tasks and handle high-dimensional data. The study investigates CNN requirements, develops a prototype system, and evaluates its accuracy, achieving an impressive 86% accuracy in detecting fake facial appearances. Therefore, proving that the system can carry out the detection task may have emerged as a pivotal solution for detecting and mitigating face-spoofing attacks. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96593/ https://ir.uitm.edu.my/id/eprint/96593/1/96593.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Noh, Zakiah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Noh, Zakiah
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Bahrain, Siti Nurul Izzah Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain
description	Face anti-spoofing is a revolutionary technology involved in various aspects of daily life. Specifically, facial anti-spoofing is a detection process that involves using printed or even keepsakes to mimic genuine facial appearances, and it is related to the facial detection application. The problems that face anti-spoofing are the need for security enhancement, the lack of biometric authentication, and the system's vulnerabilities in manipulating facial detection. In this project, the Convolutional Neural Network (CNN) algorithm was implemented using TensorFlow in Python to detect fake face images. The model facilitated a straightforward construction of the CNN, allowing for sequential handling of inputs. The model included Conv2D and MaxPooling2D layers for feature extraction, followed by a flattened layer and a dense layer with dense, dropout, and batch normalization layers. This project is due to its ability to do face detection and anti-spoofing tasks and handle high-dimensional data. The study investigates CNN requirements, develops a prototype system, and evaluates its accuracy, achieving an impressive 86% accuracy in detecting fake facial appearances. Therefore, proving that the system can carry out the detection task may have emerged as a pivotal solution for detecting and mitigating face-spoofing attacks.
format	Thesis
qualification_level	Bachelor degree
author	Bahrain, Siti Nurul Izzah
author_facet	Bahrain, Siti Nurul Izzah
author_sort	Bahrain, Siti Nurul Izzah
title	Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain
title_short	Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain
title_full	Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain
title_fullStr	Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain
title_full_unstemmed	Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain
title_sort	face anti-spoofing using convolutional neural networks / siti nurul izzah bahrain
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96593/1/96593.pdf
_version_	1804889997767081984

Face anti-spoofing using Convolutional Neural Networks / Siti Nurul Izzah Bahrain

Similar Items