Artificial Intelligence-Based Face Recognition Using Deep Learning - A Review of Models, System Design, Applications and Challenges

Face recognition has become one of the most widely deployed applications of artificial intelligence, supporting smartphone unlocking, attendance management, banking and border control. Deep learning has replaced hand-crafted features with representations learned directly from large collections of face images, producing large gains in accuracy under unconstrained conditions. This paper presents a structured review of artificial intelligence-based face recognition using deep learning. It explains the stages of a recognition pipeline, compares the main families of deep models and training objectives, proposes a modular system architecture with a liveness check, and summarizes datasets and evaluation measures. Application areas, including education and financial services, are discussed together with the main challenges: pose and occlusion, spoofing, demographic bias, adversarial attacks and privacy. The paper closes with emerging directions such as self-supervised learning, synthetic training data, federated learning and privacy-preserving matching, and offers practical recommendations for responsible deployment