From a specialised biometric technique, face recognition technology has quickly become a commonplace part of artificial intelligence systems. This article examines the theoretical underpinnings, algorithmic developments, and real-world uses of face recognition. It charts the evolution of robust, scalable models utilising triplet loss, transformer-based architectures, and convolutional neural networks (CNNs). Alongside key assessment metrics and benchmark datasets, core system components such as preprocessing, feature extraction, embedding generation, and classification are investigated. The study illustrates practical applications across industries such as digital identity verification, retail, healthcare, and security, demonstrating the technology's expanding influence and its impact on important issues. A fair perspective on upcoming developments is provided by discussing ethical issues and new regulatory systems. The study also looks at how facial recognition is used in marketing, particularly in payment systems. For academics, practitioners, and politicians seeking to understand and develop face recognition systems across the scientific and societal spheres, this work serves as an extensive reference.
Ademi et al. (Wed,) studied this question.