Published Paper Details:

The virtual trial room system operates by receiving real-time video feeds from camera and processing the data using the OpenCV computer vision library and Haar Cascade classifier. To ensure accurate face identification in the video feed, the Haar Cascade classifier undergoes training on a substantial dataset of human faces. OpenCV's algorithms extract attributes from detected faces and monitor their movements in real-time. Distinguishing itself from typical security systems that require physical security officers, the virtual trial room system functions autonomously, thus reducing the need for additional staff and associated costs. This autonomy makes it well-suited for various environments, including office buildings, public spaces, and retail establishments where real-time tracking and monitoring are essential for safety and security. Notably, the system's affordability stands out as one of its key advantages. Moreover, the system excels in real-time tracking and monitoring, crucial for maintaining building safety and security. Its capability to reliably detect and track human faces even in challenging lighting conditions is owed to the utilization of advanced computer vision techniques like the Haar Cascade classifier and OpenCV. Additionally, the system is highly expandable, allowing for the integration of additional cameras and sensors as required. Data gathered by the system is securely stored and managed using Dlib, ensuring efficient data storage and retrieval for analysis purposes. Additionally, the virtual trial room system can be programmed to send notifications if it detects any unexpected movements or behaviors, enabling proactive measures to be taken and enhancing overall security protocols.