부산대학교 도서관

This study uses a CNN model to explore deeper into the complex categorization of mask face emotion identification. There are number of application techniques which uses face emotion recognition [FER]. Ex.: - application like (Snapchats), Healthcare (identify genetic diseases), Market research, phone (face locks). The problem over this application is still under the accuracy point of research. Mask Facial Emotion based on image processing and vision of computer. In this project we are presenting the system which run on machine learning algorithm [CNN (Convolutional neural network)]. It is capable of recognition of facial emotion under mask with more accuracy. This system helps to differentiate between 5 universal emotions like Happy, Anger, neutral, Surprise and Sad.