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ones algorithm. The pre-processing stage involves scaling, noise-removal median filtering, and grayscale conversion to improve the image quality. Next comes contrast-limited adaptive histogram equalization. The system uses principal component analysis (PCA) and an expanded local binary pattern (LBP) to extract information from the face photos in order to recognize faces. By lessening the effect of illumination changes, the improved LBP hopes to raise identification rates. The features that were derived from test and training photos are then contrasted. To identify and recognize facial photos, the system combines the upgraded LBP algorithm with PCA, choosing the optimal outcome. When a student is successfully identified, the system logs their presence and saves the data in an Excel file. Furthermore, the technology has the ability to simultaneously recognize every student in a class when used with CCTV cameras with high megapixel counts. It's important to note that the solution presented here concentrates on the technical facets of attendance tracking and facial recognition. Additional factors, like data protection, permission, and ethics, should be considered for an effective deployment.
GK et al. (Sat,) studied this question.