Ensuring worker safety is a basic concern in high-risk areas like construction sites, where accurate and efficient human identification is essential. Accurate identification of personnel at worksites is crucial for reducing security hazards. Contemporary approaches exhibit low identification rates, localization inaccuracies, and elevated latency, hence jeopardizing safety. This paper presents SECURITY YARDS, a framework utilizing cloud computing and the Internet of Things (IoT) for human identification through gait recognition. Our approach utilizes Speeded Up Robust Features (SURF) and Convolutional Neural Networks (CNNs) to extract and classify gait characteristics, providing a more nuanced and adaptable representation. A Support Vector Machine (SVM) at the Fog level optimizes classification, improving precision and processing efficacy. Final decision-making, data storage, and monitoring are conducted on the cloud, ensuring scalability and real-time safety management. The findings indicate that SECURITY YARDS markedly enhances accuracy and lowers latency relative to traditional biometric identification systems. This research advances the creation of more dependable and adaptive safety solutions, facilitating future improvements in worksite security and monitoring.