Face recognition is a pivotal domain within computer vision, focused on verifying and identifying individuals using images or videos. People find applications across diverse fields, including security systems, biometrics, attendance tracking, and timekeeping. Significant advancements in face recognition techniques have been achieved, with deep learning and Neural Network approaches demonstrating superior accuracy. This paper presents the development of a face recognition system designed for student attendance monitoring in classrooms, utilizing the Multi-task Convolutional Neural Network (MTCNN) model. This advanced recognition technology offers rapid, accurate, and efficient identification or verification. As a result, the use of facial recognition systems in educational institutions has garnered considerable interest among administrators for its convenience, the ability to deliver swift and reliable solutions for tracking and recording student attendance.