Advancements in Pupil Diameter Estimation Using Webcam Images, Federated Learning for Anomaly Detection, and Innovative Image Processing Techniques: A Comprehensive Survey

Abstract

The rapid growth of machine learning and computer vision has led to the emergence of new techniques for addressing critical challenges across multiple domains, including health diagnostics, data privacy, and visual media generation. This paper provides a comprehensive overview of recent developments in three key areas: pupil diameter estimation from webcam images, federated learning for unsupervised anomaly detection, and innovative image processing techniques such as seamless panorama generation and layout-to-image transformation. Specifically, the study explores how webcam-based datasets, such as those designed for pupil measurement, have expanded the scope of eye-tracking applications by eliminating the need for specialized hardware. Furthermore, the application of upscaling techniques to improve pupil diameter prediction accuracy is analyzed in detail. In the realm of federated learning, this paper investigates the development of benchmarks tailored to decentralized, privacy-preserving anomaly detection in tabular data, emphasizing the growing importance of data security and privacy in distributed systems. Lastly, the paper reviews state-of-the-art image processing methodologies, including the generation of seamless panoramas over time and curriculum learning techniques that progressively blur objects for improved layout-to-image generation. By synthesizing insights from these diverse fields, this survey highlights the intersections of machine learning, privacy concerns, and image generation technologies, offering a forward-looking perspective on their potential applications. The works reviewed herein provide a critical foundation for ongoing and future research aimed at solving real-world problems through these advanced techniques.