Enhanced Face Authentication With Separate Loss Functions

TL;DR

This paper proposes a facial authentication system with four components, focusing on improving face recognition accuracy and anti-spoofing by developing two new loss functions, LMCot and Double Loss, to enhance face verification performance.

Contribution

The paper introduces two novel loss functions, LMCot and Double Loss, specifically designed to improve face recognition and anti-spoofing in facial authentication systems.

Findings

LMCot and Double Loss improve recognition accuracy.

Enhanced anti-spoofing capabilities demonstrated.

System achieves higher reliability in face authentication.

Abstract

The overall objective of the main project is to propose and develop a system of facial authentication in unlocking phones or applications in phones using facial recognition. The system will include four separate architectures: face detection, face recognition, face spoofing, and classification of closed eyes. In which, we consider the problem of face recognition to be the most important, determining the true identity of the person standing in front of the screen with absolute accuracy is what facial recognition systems need to achieve. Along with the development of the face recognition problem, the problem of the anti-fake face is also gradually becoming popular and equally important. Our goal is to propose and develop two loss functions: LMCot and Double Loss. Then apply them to the face authentication process.