AI-Based Human Face Recognition System

Abstract

The Human face recognition systems have undergone significant advancements in the last decade due to the rapid development of artificial intelligence (AI) and deep learning technologies. Today those systems are being used in all types of applications, from security and authentication to surveillance, personalized services through healthcare and even entertainment. The transformation from conventional hand-crafted features to deep learning (DL) based methods, exemplified by convolutional neural networks (CNNs), has yielded tremendous advancement in face recognition accuracy and efficiency that made it possible for large-scale real-time deployment in many applications. In this article, a detailed review of IoT and facial recognition with AI has been given for the past few years. Models from Eigen faces to Face Net, Deep Face and Arc face evolve over time. We analyze the impact of large-scale facial datasets and pre-trained models that have propelled the performance of these systems to near-human accuracy in challenging conditions like varying lighting, occlusion, and pose. Additionally, we compare state-of-the-art techniques in terms of performance metrics and their ability to handle real-world complexities. We also discuss the role of transfer learning, multi-task learning, and lightweight models, which have enabled face recognition systems to be deployed on edge devices and mobile platforms, offering real-time processing with minimal computational resources. Moreover, this paper explores the challenges that persist in face recognition, such as issues of fairness, bias, privacy concerns, and vulnerability to adversarial attacks, which have raised ethical and security concerns.Finally, we identify future research directions, including improving robustness in unconstrained environments, mitigating biases in face recognition datasets, and enhancing the privacy and security of AI-based systems. As face recognition continues to expand in scope and applications, it remains essential to address these challenges to ensure the technology is both effective and ethically aligned with societal values.