Implementation of Facial Recognition System for Metaverse using sbRIO FPGA and NB-IOT Module

Peng Lu; Meijiao Yu; Renyong Zhang

doi:10.54097/ajst.v6i1.8275

Authors

Peng Lu
Meijiao Yu
Renyong Zhang

DOI:

https://doi.org/10.54097/ajst.v6i1.8275

Keywords:

Metaverse, FPGA, DeepFace, NB-IOT.

Abstract

Facial recognition is crucial for identity verification in the metaverse, but it requires significant processing and operation overhead. Transmitting high-definition images to a server PC for processing is not feasible in low-capacity, low-bandwidth, or low-processor virtual environments. To overcome these challenges, we developed a narrow-bandwidth framework integrating embedded FPGA technology with a low-power NB-IOTcommunication module. Our approach uses a DNN-based DeepFace model with front face detection and 7-layer DNN convolution result extraction performed on the Zynq FPGA chip of sbRIO , reducing computational overhead and enabling efficient processing. By leveraging NB-IOT's remote transmission capabilities, classification data is transmitted back to the local server for comparison. Our proposed framework improves speed and accuracy while overcoming bandwidth and processing power challenges, making it a promising solution for facial recognition in immersive virtual environments.

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