Enhancing Face Recognition Accuracy Using Digital Twins

Abstract

In today's scenario face recognition systems have become increasingly prevalent in various domains, including security, surveillance, and identity verification. However, challenges persist in achieving high accuracy, especially in scenarios with variations in lighting, pose, and facial expressions. Digital twins, virtual representations of physical objects or entities, offer a promising avenue to address these challenges by providing a simulated environment for training and testing face recognition algorithms. This research explores the potential of digital twins in enhancing face recognition accuracy. Specifically, we investigate the effectiveness of incorporating digital twins into deep learning approaches for face recognition. Through a comparative study of different techniques and architectures, we assess the impact of digital twins on recognition performance across diverse datasets and conditions. The findings of this study contribute to advancing the state-of-the-art in face recognition technology and offer insights into leveraging digital twins to improve accuracy and robustness in real-world applications.