A Novel Approach to Identify Facial Expression Using CNN

Abstract

Facial expression recognition (FER) has been one of the actively research topics due to its wide range of application. FER is a very challenging task because of less training datasets. The result of facial expression is the well-classified loss function based on the robust prior knowledge at the end-to-end neural network architecture. The proposed methodology is able to address the task of facial expression recognition and aim to classify images of faces into five discrete emotion categories (happy, sad, angry, neutral, and surprise). Result of this paper is compared with the multiple training datasets and return the maximum appeared face emotion and with highest accuracy. The efficiency a well as the effectiveness of the proposed methodology is more accurate.