Enhancing 2D face recognition systems: addressing yaw poses and occlusions with masks, glasses, and both

Biometric identification in general and face recognition in particular are used to solve a great number of tasks, both security-related and related to device authentication. Although research in face recognition is state-of-the-art today, real face recognition systems still have real problems in real environments, for example, the problems of pose variation and oc-clusion. In particular, the given paper is devoted to the study of the effects of 2D face recognition depending on the yaw angles and occlusions that include masks and glasses or their combination. In this regard, the UPM dataset is employed to compare the face recognition models using MTCNN, FaceNet, SVC, MLP, and the ensemble model with the hard voting mechanism for the final decision. The following will be used in the assessment; accuracy, F1 score, confusion, classification matrix, and ROC curve. These outcomes reveal the variations in the recognition efficiency in the context of different occlusion circumstances, along with prospects and limitations concerning their use.

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