Evaluating Network Performance in a Web-Based Augmented Reality System for Lipmatte Color Recommendation

Abstract

This study presents the Augmented Reality (AR) Lipmatte Recommendation System aimed at enhancing user experience and optimizing performance within the beauty industry. By utilizing AR technology, the system offers real-time virtual lipstick try-ons and personalized shade suggestions based on users' skin tones. Key functionalities include user registration and secure authentication, ensuring personalized and protected access. The system's performance focuses on delivering precise AR overlays for virtual try-ons, emphasizing low latency and seamless interaction across varied network environments. Network performance was analyzed in two scenarios: client-to-server data transfer and resource loading. Performance metrics indicated that the system efficiently managed increased network traffic and resource demands, demonstrating scalability and responsiveness. For instance, the average network transfer rate increased from 1.29 KB/s with four devices to 2.47 KB/s with twenty devices, confirming the system's ability to handle larger data flows efficiently. Similarly, resource loading times varied, with an average loading time of 3.76 ms for four devices, improving to 2.44 ms with eight devices, and peaking at 3.96 ms with sixteen devices before stabilizing at 2.80 ms with twenty devices. These findings underscore the necessity of a robust network infrastructure to ensure a seamless AR experience, which is vital for enhancing consumer engagement, brand loyalty, and purchasing decisions in beauty applications. This research highlights the significant potential of AR technology in modernizing the beauty shopping experience while illustrating the critical role of network performance in achieving optimal user satisfaction. Future investigations should explore advanced dynamic resource allocation algorithms and emerging technologies, such as 5G connectivity and edge computing, to further enhance real-time AR applications and better understand user interactions with AR in retail settings.