Face Recognition Attendance Framework using Quantum Edge Processing

Through the use of a Streamlit web application, the script is a highly intelligent tool that combines cutting-edge quantum processing principles with traditional computer vision approaches to handle a variety of face detection and identification tasks. While other libraries handle computer vision jobs and numerical calculations, such as NumPy and OpenCV, Streamlit is essential to developing the web application interface. While Streamlit WebRTC allows real-time video streaming, PIL is utilized for image processing. The application is set up to have a personalized look and a centered layout. Users may upload photographs, choose detection modes, and modify face detection settings on its header and sidebar. The program detects faces in uploaded photographs by encircling them with bounding boxes using the Haar Cascade Classifier. This may be used to build a face dataset or train a face recognition algorithm. Another important feature is real-time face identification, in which the software uses the Haar Cascade Classifier to identify faces in a video stream. Bounding boxes are used to emphasize faces, and photos containing faces are retained for later study or training. The program may utilize taken photographs to train a model for face recognition. These photos are processed, turned into grayscale, and then the LBPH (Local Binary Patterns Histograms) technique is used to train the model. The trained model is then kept for use in upcoming facial recognition assignments. Moreover, the program may be set up to track attendance in real time by identifying faces in a webcam stream. Time stamps are recorded, attendance is logged using recognized faces, and the information is saved to a CSV file. All things considered, the script provides a complete solution that combines traditional and quantum methods for real-time face identification, picture processing, and attendance monitoring. This makes it an adaptable tool for a range of computer vision and machine learning applications. Accuracy is improved by the incorporation of Quantum Edge Processing into the described face recognition framework, especially under difficult circumstances like dim lighting, spectacle wear, or other facial changes. The approach improves the identification of important face characteristics beyond what is possible with conventional techniques by utilizing quantum parallelism to achieve more accurate and efficient edge detection. Consequently, the method greatly enhances performance in real-time settings, where traditional systems frequently encounter constraints.