Machine learning model for emotion detection and recognition using an enhanced Convolutional Neural Network

Abstract

Emotion expression recognition has been a challenging task in recent years due to large intra-class variation and persistent difficulty. Most studies fail on datasets with image variations and partial faces but work best on controlled datasets. Recent work using deep learning models has improved emotion recognition by developing mini-Xception based on Xception and Convolution Neural Network (CNN). This system can focus on important parts like the face, performing face recognition, and emotion classification simultaneously. A visualization method is used to distinguish between different emotions based on the classifier results. An experimental study on the FER-2013 dataset demonstrated that the mini-Xception algorithm successfully performed all tasks, including emotion recognition and classification, with an accuracy of approximately 95.60%.