The paper critically reviews face recognition models that are based on deep learning, specifically security and surveillance. Existing systems are susceptible to pose variation, occlusion, low resolution and even aging, even though they perform quite well under controlled conditions. The authors make a systematic review of four state-of-the-art architectures—FaceNet, ArcFace, OpenFace and SFace—through the use of five benchmark datasets, namely LFW, CPLFW, CALFW, AgeDB-30 and QMUL-SurvFace. The measures of performance are evaluated as the area under the receiver operating characteristic (ROC-AUC), accuracy, precision and F1-score. The results reflect that FaceNet and ArcFace achieve the highest accuracy under well-lit and frontal settings; when comparing SFace, this proved to have better robustness to degraded and low-resolution surveillance images. This shows the weaknesses of traditional embedding methods because bigger data sizes reduce the performance of OpenFace with all of the datasets. These results underscore the main point of this study: a comparative study of the models in difficult real life conditions and the observation of the trade-off between generalization and specialization inherent to any models. Specifically, the ArcFace and FaceNet models are optimized to perform well in constrained settings and SFace in the wild ones. This means that the selection of models must be closely monitored with respect to deployment contexts, and future studies should focus on the study of architectures that maintain performance even with fluctuating conditions in the form of the hybrid architectures.
Zhalgas et al. (2025) studied this question.