FACIAL RECOGNITION SYSTEM FOR DISTANCE LEARNING STUDENT ATTENDANCE MANAGEMENT USING MACHINE LEARNING

Abstract

The administration of student attendance constitutes a vital component of academic governance, affecting both educational outcomes and institutional efficacy. Utilizing machine learning to augment precision and efficacy, with adaptability for both physical and remote learning environments. The research methodology encompasses the acquisition of facial data from students under diverse lighting conditions, perspectives, and remote settings, succeeded by preprocessing and training of a facial recognition algorithm employing machine learning techniques. The system addresses key technical challenges such as camera quality variations, lighting inconsistencies, and spoofing risks by integrating robust image preprocessing and security safeguards. Quantitative evaluation shows that under ideal and controlled conditions, the system achieves up to 100% accuracy with an average processing time of 0.8 seconds. With the specifications Intel Core i5, RAM8 GB, minimum windows 10, NVIDIA GeForce GTX 1050, 1080p minimum camera with 30 fps frame rate, standard CMOS sensor, and automatic exposure adjustment capabilities, accuracy will drop if the conditions are not ideal. The system ensures the security and privacy of student facial because it is live with zoom or LMS. Furthermore, the incorporation of this system facilitates the realization of smart campus initiatives by delivering precise, real-time attendance information. This inquiry contributes to educational technology, enhancing operational efficacy and fostering digital transformation within higher education institutions. The designed system also seeks to reduce overall student attendance fraud.