Metaverse-Aware Avatar Face Detection Model Using Deep Learning

With the rapid advancement of virtual reality and online virtual communities, the concept of the metaverse has gained significant attention. The metaverse refers to a collective virtual shared space where users interact with each other and experience a sense of presence in a virtual environment. Avatars, which serve as the virtual representations of users, play a crucial role in bridging the gap between the physical and virtual worlds. In this context, accurate and dynamic avatar features become essential to enhance user engagement and foster a more immersive experience. This paper extends the traditional scope of avatar face detection by incorporating the concept of the metaverse; and provides a comprehensive applying and analysis of avatar face detection, focusing on the YOLOv8. We evaluate these techniques using dataset obtained from different sites from internet. By incorporating the metaverse concept into avatar face detection, we aim to provide realistic and responsive avatar representations that enable users to express themselves more authentically in virtual environments. The results indicate that YOLOv8 has a higher detection rate and the detection rate of YOLOv8 was 98%.