Deep Learning Based Facial Emotion Recognition Using CNN and ResNet18 Architectures

This research presented a deep learning-based facial emotion recognition framework using CNN and ResNet18 architectures. Experimental evaluation demonstrated that the proposed optimized model achieved 90% classification accuracy, outperforming the baseline CNN and ResNet18 models. The proposed system can be effectively applied in intelligent tutoring systems, healthcare applications, adaptive learning environments, and human-computer interaction systems. Future work may include: ·                  Vision Transformers (ViT) ·                  Attention mechanisms ·                  Real-time FER deployment ·                  Multimodal emotion recognition ·                  Federated learning-based FER systems

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