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.
