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Key research themes
1. How can face recognition be optimized for automated attendance marking in educational settings?
This theme investigates the development and implementation of face recognition-based attendance systems aimed at automating and streamlining the attendance process in classrooms and institutions. The focus is on improving accuracy, speed, and reliability while addressing practical challenges such as real-time recognition, variations in illumination and facial expressions, and security concerns like spoofing. These systems leverage computer vision algorithms, machine learning techniques, and hardware platforms like Raspberry Pi to provide scalable, contactless, and efficient attendance solutions.
Key finding: This study developed an automatic attendance system using face detection via Viola-Jones cascade classifier and recognition with PCA for feature selection and SVM for classification, enabling real-time classroom attendance... Read more
Key finding: Implemented a face recognition-based attendance system utilizing Histogram of Oriented Gradients (HOG) for face encoding and recognition, achieving fast and precise identification of students via video streaming. The system... Read more
Key finding: Proposed a facial recognition attendance system enhanced with real-time liveness detection using YOLOv5 to prevent spoofing attacks from images or videos. The system integrated attendance logging and absentee notification via... Read more
Key finding: Developed a face recognition attendance tool using Local Binary Pattern Histogram (LBPH) for feature extraction and matched faces to a database to record attendance automatically. The system demonstrated that biometric face... Read more
Key finding: Reviewed and implemented a low-cost, portable face recognition attendance system based on Raspberry Pi and OpenCV using Haar Cascade and Histogram of Oriented Gradients (HOG) methods. The system emphasized real-time detection... Read more
2. What biometric technologies are most effective for eliminating proxy attendance and ensuring secure, reliable student attendance tracking?
This theme explores biometric modalities beyond facial recognition—such as fingerprint and iris scanning—as well as RFID and QR code-based systems, evaluating their accuracy, security, speed, and practicality in attendance monitoring. Research compares traditional manual attendance methods with biometric-enhanced systems, focusing on error reduction, elimination of proxy attendance, and streamlining administrative overhead. The goal is to identify biometric solutions appropriate for various institutional needs, balancing technical efficacy, cost, and user acceptance.
Key finding: Designed and deployed a fingerprint biometric attendance system that captures and verifies unique fingerprint data for accurate student authentication, effectively eliminating impersonation and proxy attendance. The system... Read more
Key finding: Developed a fingerprint recognition attendance system integrated with cloud-based data storage and mobile applications for real-time attendance monitoring at ESPAM Formation University. The study demonstrated high security,... Read more
Key finding: Implemented an RFID-based attendance system replacing pen-and-paper methods to address issues like human error and time inefficiencies. The system utilized RFID tags and readers to speed up attendance logging while improving... Read more
Key finding: Developed an Android-based lecturer attendance system employing QR codes for rapid, error-resistant attendance recording. The system connected attendance data with administrative modules like payroll and finance, improving... Read more
Key finding: Provided a comprehensive review of biometric technologies including palmprint, fingerprint, iris, facial recognition, RFID, Bluetooth, and QR code for attendance systems, highlighting their roles in automating attendance. The... Read more
3. How can machine learning and decision support systems improve the predictive analytics and administrative decision-making based on attendance data?
This theme addresses the integration of attendance tracking with advanced analytics, machine learning (ML), and decision support system (DSS) frameworks to enhance institutional management. By leveraging ML algorithms on attendance datasets—particularly those collected through biometric or face recognition systems—researchers seek to predict individual performance (such as teacher effectiveness), optimize attendance management strategies, and enable data-driven decision-making. Studies explore model development, evaluation, and the application of theoretical frameworks like DSS for organizational benefit.
Key finding: Developed an integrated predictive model combining face recognition attendance data with decision support system (DSS) theory and machine learning algorithms (SVM, decision trees, random forest) to identify high-performing... Read more
Key finding: Proposed a web-based system that automates student attendance tracking alongside detailed analytics and reporting capabilities. The system ensures real-time monitoring, proxy prevention, and integration with institutional... Read more