This research paper introduces a Python-based implementation of a facial recognition system utilizing face recognition and Open CV libraries. The system has diverse applications, including security, surveillance, social media, and entertainment. By employing a pre-trained neural network, the system enables users to select an image file and accurately detect and recognize faces within it. Additionally, it includes functionality to encode faces from a specified folder, enabling comparison against the faces in the target image. By employing a blend of face detection, feature extraction, and machine learning algorithms, the system effectively identifies, and labels faces in images. Its effectiveness illustrates the potential of facial recognition technology for numerous applications, while also underscoring the ethical and privacy concerns associated with such technology. Addressing these issues and developing more precise and dependable systems would require further research. The primary objective of the system is to offer an improved solution for classroom attendance, ensuring the avoidance of false attendance records and significantly reducing the time required for attendance taking, through the application of machine learning solutions.