ANALYSIS OF FACIAL AUTHENTICATION SYSTEMS FOR NEURAL NETWORK MODIFICATION OF RAW BIOMETRIC DATA
DOI:
https://doi.org/10.17605/OSF.IO/RZMFBKeywords:
authentication, neural network, deep learning, FAR, FRR, feature-based methods, state-of-the-art deep learning approachesAbstract
Facial authentication systems have gained widespread adoption due to their convenience and effectiveness. Recent advancements in deep learning techniques have sparked interest in exploring neural network modifications for enhancing the performance of facial authentication systems. This scientific article presents a comprehensive analysis of existing facial authentication systems and investigates the benefits of neural network modifications applied to raw biometric data. The review encompasses traditional feature-based methods as well as state-of-the-art deep learning approaches. The strengths, limitations, and performance metrics such as accuracy, false acceptance rate (FAR), false rejection rate (FRR), and execution time are evaluated. Additionally, the potential of neural network modifications for improving facial authentication systems is discussed, providing valuable insights for future research and development in this field.
