Deep learning has substantially enhanced facial emotion recognition, an essential element of human--computer interaction. This study evaluates the performance of multiple architectures, including a custom CNN, VGG-16, ResNet-50, and a hybrid CNN-LSTM framework, across FER2013 and CK+ datasets. Preprocessing steps involved grayscale conversion, image resizing, and pixel normalization. Experimental results show that ResNet-50 achieved the highest accuracy on FER2013 (76.85%), while the hybrid CNN-LSTM model attained superior performance on CK+ (92.30%). Performance metrics such as precision, recall, and F1-score were used for evaluation. Findings highlight the trade-off between computational e... More >