Facial Expression Recognition Using Convolutional Neural Network

Facial expression recognition is important for many domains such as schools Komagal and Yogameena (PTZ-Camera-Based Facial Expression Analysis using Faster R-CNN for Student Engagement Recognition. IEEE Access, 2023), hotels, and surveys. Facial expression recognition is being used for many applications, such as evaluating student understanding of a subject Komagal and Yogameena (PTZ-Camera-Based Facial Expression Analysis using Faster R-CNN for Student Engagement Recognition. IEEE Access, 2023), the health condition of patients in hospitals, customer satisfaction in hotels and restaurants, etc. In this paper, we have designed different convolutional neural networks (CNNs) for the recognition of seven facial expressions. We have achieved 96.35 testing accuracy with CNN having three pairs of convolution and max pooling on the Ryerson Audio-visible Database of Emotional Speech and Music, consisting of seven emotion datasets.