Advancing Face Recognition with Hybrid CNN-ViT-MLP: A Comparative Study

Deep learning, which includes Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), has greatly advanced face recognition. A hybrid CNN-ViT-MLP model is presented in this study, and its performance is compared with those of cutting-edge facial recognition architectures such as ViTs, FaceNet, and ResNet-50. CNNs for local feature extraction, ViTs for global feature representation, and an MLP classifier for ultimate decision-making are all integrated into the suggested hybrid model. According to the findings, the hybrid model outperforms standalone structures in terms of accuracy, robustness, and computational efficiency. Furthermore, its efficacy in practical applications is confirmed by comprehensive experiments conducted on large-scale datasets. Analysis of the model's outputs shows that, under different circumstances, it performs better than conventional models