Natural settings make it challenging to identify facial expressions since head position, illumination level, and ‎‎occlusion vary. Thus, developing a more generic model without front-facing images alone is quite crucial. This ‎research proposes a facial expression ‎recognition model based on pre-trained deep convolutional neural networks ‎with transfer learning. The model was trained ‎on several cases to classify face expressions into seven ‎classifications efficiently. The proposed system used the EfficientNetB0 model ‎that has one dense dropout layer. The model first rescales and norms the input dataset in the input ‎layer that takes images of a larger resolution to get better results. After entering 7 blocks sequential ‎in each one, the data convolution two times, then speeding up training and avoiding overfitting by ‎adding a dropout layer and batch normalization layer. The model achieves an accuracy of 70.60% when features are frozen, and the ‎classifier is unfrozen. In contrast, the Fine ‎Tune model achieves the highest accuracy, 72.69%, by unfreezing the feature extractor and ‎training the entire model. ‎