Interpreting facial expressions is an important task for human beings since they convey their inner feelings through facial expressions. Then, facial expressions are significant visual signals to recognize human emotions. They are used in human communication and machines to build a good interaction system by analyzing human emotional behaviors. Therefore, facial expression recognition is an important study in Human-Computer Interaction. Compared to the past, which focused on traditional feature extraction methods for facial expression recognition, the current state of the arts emphasizes deep learning based approaches. The drawback of deep learning based methods is that they require a massive amount of data. Therefore, in this study, we apply transfer learning to the pre-trained deep learning models to recognize the facial expression and compare their results. The experiments were conducted on the Extended Cohn Kanade Facial Expressions Dataset (CK+), and approximately 92% accuracy in facial expression recognition was obtained with the EfficientNet B0 model. In addition, the discriminatory facial expression features of the model were reported.