Recent advancements in Face Recognition (FR) highlight key evolutions in loss functions, focusing on the transition from classical models to contemporary approaches. FR technology has found applications across sectors, including finance, cybersecurity, and surveillance, demonstrating its wide-ranging impact. The review primarily emphasizes prominent loss functions such as ArcFace, which has emerged as a leading method, followed by less frequently employed alternatives like CosFace and FaceNet. The article underscores the critical role of preprocessing, including detection and alignment, essential for maximizing the efficiency of FR models, particularly in training scenarios.
Significant advancements in Face Recognition technology are evident, with a particular emphasis on the evolution of loss functions like ArcFace and CosFace.
Face Recognition applies to various fields such as financial services, cybersecurity, and video surveillance, highlighting its importance in modern applications.
Collection
[
|
...
]