Abstract:
Face recognition technology has witnessed significant advancements in recent years, propelled by the rapid development of artificial intelligence (AI) techniques. This paper presents a comprehensive review of the latest methodologies, algorithms, and applications in the field of face recognition powered by AI .First, we delve into the foundational concepts of face recognition, discussing traditional techniques and their limitations. Subsequently, we explore how the integration of AI, particularly deep learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has revolutionized face recognition systems by enabling more accurate, robust, and scalable solutions.We analyze various approaches to face recognition using AI, including face detection, feature extraction, and classification. Additionally, we examine the role of data augmentation, transfer learning, and ensemble methods in enhancing the performance of AI-based face recognition models.Moreover, this review discusses the challenges and ethical considerations associated with AI-driven face recognition systems, such as privacy concerns, bias mitigation, and the need for transparent decision-making processes.Furthermore, we highlight emerging trends and future directions in the field, including multimodal biometrics fusion, continual learning, and the integration of explainable AI techniques for enhanced interpretability and trustworthiness.Overall, this review provides valuable insights into the state-of-the-art in face recognition technology powered by artificial intelligence, offering guidance for researchers, practitioners, and policymakers interested in leveraging these advancements for various applications, ranging from security and surveillance to human-computer interaction and personalized services.
