Why the study?
This study aimed to develop and analyze facial recognition patterns using Principal Component Analysis to verify student presence and ascertain attendance in class.
A facial recognition system using PCA and Raspberry Pi achieved 95% accuracy for student attendance tracking.
Supports biometric attendance tools in education; leaves open validation of accuracy and scalability in real settings.
This study aims to develop and analyze facial recognition patterns, a biometric-based approach to identify individuals through facial features. Such biometric patterns can be used as a tool to verify the presence of a student. Face recognition technology utilizing Raspberry Pi to assess the appropriateness of biometric patterns. The facial matching process is employed to ascertain student attendance in class. The Principal Component Analysis (PCA) technique is employed in the face detection procedure. The sample testing was carried out on a group of fifty students, with each student supplying thirty different facial photos. 1,500 images were processed utilizing the PCA algorithm within the OpenCV library. A 95% accuracy rate was achieved with the photos that were obtained, which have a resolution of 640x480 pixels.
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Desmira et al. (2025) studied this question.
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