Face Emotion Recognization Using Dataset Augmentation Based on Neural Network

TL;DR

This paper proposes a hybrid data augmentation method to improve facial emotion recognition accuracy using neural networks on public datasets.

Contribution

It introduces a novel data augmentation technique combined with benchmark models for enhanced facial emotion recognition performance.

Findings

Improved recognition accuracy on public datasets

Effective augmentation method enhances model robustness

Benchmark models show significant performance gains

Abstract

Facial expression is one of the most external indications of a person's feelings and emotions. In daily conversation, according to the psychologist, only 7% and 38% of information is communicated through words and sounds respective, while up to 55% is through facial expression. It plays an important role in coordinating interpersonal relationships. Ekman and Friesen recognized six essential emotions in the nineteenth century depending on a cross-cultural study, which indicated that people feel each basic emotion in the same fashion despite culture. As a branch of the field of analyzing sentiment, facial expression recognition offers broad application prospects in a variety of domains, including the interaction between humans and computers, healthcare, and behavior monitoring. Therefore, many researchers have devoted themselves to facial expression recognition. In this paper, an effective hybrid data augmentation method is used. This approach is operated on two public datasets, and four benchmark models see some remarkable results.