IEEE International Conference on Computer, Electrical & Communication Engineering (ICCECE)

Abstract

The integration of facial recognition technology with Industrial Internet of Things (IIoT) systems offers significant potential to enhance operational efficiency, security, and safety within industrial environments. Automating processes like access control and attendance tracking minimizes human intervention, reduces errors, and streamlines workflows. From a security perspective, facial recognition strengthens access management, reduces unauthorized entries, and enhances real-time surveillance capabilities. In safety-critical industries, this technology can enforce compliance with protective protocols and monitor workers' well-being by detecting signs of fatigue or stress. Additionally, the use of artificial intelligence (AI) and machine learning facilitates real-time data analysis, providing insights to optimize operations and prevent system failures. This paper further examines the broader applications of IoT-driven facial recognition, highlighting promising research areas such as intelligent security systems, biometric authentication, automated surveillance, and smart access control. Practical implementations span smart homes, healthcare monitoring, financial transactions, and public safety, where real time facial identification enhances both security and operational performance. Despite these advantages, challenges like data privacy concerns and the need for robust technological infrastructure remain critical. Strategies such as data encryption, edge computing, and adherence to privacy regulations like GDPR are essential to protect personal information. As advancements in AI, machine learning, and 5G networks continue, facial recognition systems integrated with IIoT will become increasingly accurate and efficient, paving the way for smarter, more secure, and highly efficient industrial and public environments.