Marwan Mohamed Nagah
Doctor Fatma Helmy
The main idea of this project is to study face liveness detection and how can we prevent face spoofing attacks. One of the most widely used biometric approaches is face recognition. Face recognition is used in many Fields such as mobile devices authentication, security access, attendance system and payment transactions. However, face recognition can be easily attacked by a method called face spoofing, That is intended to deceive the face recognition system by facial pictures obtained from images or videos. Other cheaters show the mask of an authorized person to fool the recognition camera into a real person. The proposed system is to use face liveness detection by applying different machine and deep learning algorithms such as CNN, SVM, Capsule Neural Network etc. in order to get the best accuracy between them to apply our model. Then, the system will be able to differentiate between live and fake user’s faces.
1. To build a stable system that achieve an accuracy percentage not less than 95%.
2. To detect face liveness detection and classify input images into live and spoof using the best deep learning algorithm that achieve the best results.
3. To build a Mobile Application with User-friendly interface to apply our model to it so it can be used by any organization.
The project will cover the use of deep learning and machine learning techniques. The applied model will be used as Mobile application that will be built by Flutter SDK that will be compatible for both Android and IOS devices so that everyone around the world can have access to it.
Documents and Presentations
You will find here the documents and presentation for our proposal.
You will find here the documents and presentation for our SRS.
You will find here the documents and presentation for our SDD.
You will find here the documents and presentation for our Thesis
Face Liveness Detection Using a sequential CNN technique