Masked and Unmasked Facial Recognition: Technical Appraisal of a Conventional Prototype and a Proposed Deep Learning Architecture

Aim: To appraise the documented performance and limitations of a masked/unmasked facial-recognition prototype and to specify a testable deep-learning architecture for subsequent evaluation.Design: A technical appraisal of thesis-reported prototype outcomes and a proposed but unimplemented architecture; no new comparative experiment is reported.Methods: The historical system combined an OpenCV deep-learning face detector with support vector machine (SVM)-based identity classification. The proposed system combines landmark-assisted face detection and alignment, ArcFace-style embeddings, cosine-similarity matching, an unknown-identity decision and optional ONNX Runtime inference. Evaluation requirements are defined for identity partitioning, mask conditions, multiple-person scenes and error reporting.Results: The thesis account attributes a face-detection accuracy value of 99% and approximately 80% live identity-classification accuracy to the prototype, while also presenting values of 97% for masked faces and 87% for unmasked faces elsewhere. Underlying test counts and predictions are unavailable, and these figures cannot be reconciled or recalculated. No accuracy or latency findings are available for the proposed architecture.Conclusion: A detector–embedding–matching workflow offers a technically justified route for further study, but its effectiveness, fairness, operating speed and suitability for deployment remain unestablished without reproducible evaluation.