With the aim of improving security measures in public and government buildings, this study presents a system to protect the House of Representatives using Vicon sensors and facial recognition technology to detect threats as soon as they occur and alert accordingly to take appropriate measures. A face recognition model was realized, which took advantage of a DenseNet169-based feature extractor and a dense layer-based classifier. The machine learning models and Vicon sensors used by the physical motion capture system allowed for highly accurate analysis of real-world movements. Using Decision Tree (DT) and K-Nearest Neighbors (KNN) algorithms, the system achieved optimal accuracy using a dataset that included ten categories of behaviors performed by ten employees. Thanks to the combination of motion capture and facial recognition technology, allowing for precise threat classification, the House of Representatives will have a robust security system. This research highlights the importance of technical improvements in defending public employees and facilities.