TY - JFULL AU - Nouha Khediri and Mohammed Ben Ammar and Monji Kherallah PY - 2023/3/ TI - Deep-Learning Based Approach to Facial Emotion Recognition Through Convolutional Neural Network T2 - International Journal of Computer and Information Engineering SP - 131 EP - 136 VL - 17 SN - 1307-6892 UR - https://publications.waset.org/pdf/10012968 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 194, 2023 N2 - Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. However, accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER benefiting from deep learning, especially CNN and VGG16. First, the data are pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work. ER -