Titre :	Reconnaissance biométrique par le visage
Type de document :	document multimédia
Auteurs :	Aya Lamis Hosni, Auteur ; Mourad Reguigue, Directeur de thèse
Editeur :	Laghouat : Université Amar Telidji - Département d'électronique
Année de publication :	2024
Importance :	60p.
Accompagnement :	cd rom
Note générale :	Systèmes de Télécommunications
Langues :	Français
Mots-clés :	Convolutional Neural Networks (CNN)  digital image  Deep Learning.
Résumé :	Given the weaknesses of traditional protection methods nowadays, such as passwords and PINs, which have become insufficient and vulnerable to hacking, theft, and other threats, scientists have turned to finding new and more secure ways to verify and meet security and protection requirements. From this perspective, the focus has been on the human body itself, where the idea is to use unique biological features of each person as a means of verification. Among the most notable biological features are faces, as faces differ from one person to another, making them an effective means of recognition and verification. Facial recognition technology relies on analysing facial features and extracting unique characteristics for each individual, making it a powerful tool in the field of security and protection. This technology is used today in many areas, such as unlocking smartphones, identity verification in airports, and access control to sensitive areas. Facial recognition technology enhances security by reducing reliance on passwords and PINs that can be forgotten or stolen. It also provides a smoother and easier experience for users, as their identity can be recognized simply by looking at the camera. In this work, we relied on training a model using Convolutional Neural Networks (CNN) to analyze images and recognize faces. Convolutional Neural Networks are powerful tools in the field of machine learning, especially in image processing and visual pattern recognition.
note de thèses :	memoire de master en Electronique

Reconnaissance biométrique par le visage [document multimédia] / Aya Lamis Hosni, Auteur ; Mourad Reguigue, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'électronique, 2024 . - 60p. + cd rom.
Systèmes de Télécommunications
Langues : Français

Mots-clés :	Convolutional Neural Networks (CNN)  digital image  Deep Learning.
Résumé :	Given the weaknesses of traditional protection methods nowadays, such as passwords and PINs, which have become insufficient and vulnerable to hacking, theft, and other threats, scientists have turned to finding new and more secure ways to verify and meet security and protection requirements. From this perspective, the focus has been on the human body itself, where the idea is to use unique biological features of each person as a means of verification. Among the most notable biological features are faces, as faces differ from one person to another, making them an effective means of recognition and verification. Facial recognition technology relies on analysing facial features and extracting unique characteristics for each individual, making it a powerful tool in the field of security and protection. This technology is used today in many areas, such as unlocking smartphones, identity verification in airports, and access control to sensitive areas. Facial recognition technology enhances security by reducing reliance on passwords and PINs that can be forgotten or stolen. It also provides a smoother and easier experience for users, as their identity can be recognized simply by looking at the camera. In this work, we relied on training a model using Convolutional Neural Networks (CNN) to analyze images and recognize faces. Convolutional Neural Networks are powerful tools in the field of machine learning, especially in image processing and visual pattern recognition.
note de thèses :	memoire de master en Electronique