ATTENDANCE MONITORING USING FACE RECOGNITION

Face recognition stands as a cornerstone in image processing, particularly in attendance management systems for students. Traditional methods of attendance-taking are laborious and susceptible to manipulation. To address these issues, a novel system utilizing advanced technologies such as Haar classifiers, KNN, CNN, SVM, Generative Adversarial Networks, and Gabor filters is proposed. This system aims to digitize the attendance process, eliminating the need for manual record-keeping and mitigating proxy attendance. Following rigorous testing under various conditions like illumination and head movements, the system demonstrates robustness and efficiency. Attendance reports are automatically generated and stored in Excel format, streamlining administrative tasks. Moreover, the system proves cost-effective and requires minimal installation, making it an accessible solution for educational institutions. Leveraging automatic face recognition technologies, this system presents a real-world application for seamlessly managing student attendance, catering to the demands of modern educational settings.