Authors : Peter-Ilesanmi Folasade Victoria; Tayo P. Ogundunmade; Afolabi O. Adedamola

Volume/Issue : Volume 11 - 2026, Issue 10 - October

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Google Scholar : https://tinyurl.com/389byevh

DOI : https://doi.org/10.38124/ijisrt/26Oct667

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This study aims to explore two types of machine learning techniques, namely deep learning and probabilistic modeling, for the automatic identification of students using facial recognition technology. The study adopted an experimental design with a dataset of 620 facial images of 31 students, comparing the results of layer-wise feature extraction using the Residual Network-50 with the Gaussian Mixture Model clustering technique.

Keywords : Facial Recognition, ResNet-50, Gaussian Mixture Model, Feature Discrimination, Student Identification.

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