A comparative study of emotion recognition methods using facial expressions

TL;DR

This paper compares the performance of three advanced neural network architectures on facial emotion recognition across three datasets, highlighting their strengths and limitations in understanding facial expressions.

Contribution

It provides a comprehensive comparison of three state-of-the-art FER networks, offering insights into their effectiveness and guiding future research in emotion recognition.

Findings

Network A outperforms others on Dataset 1

Network B shows robustness across datasets

All networks improve with larger training data

Abstract

Understanding the facial expressions of our interlocutor is important to enrich the communication and to give it a depth that goes beyond the explicitly expressed. In fact, studying one's facial expression gives insight into their hidden emotion state. However, even as humans, and despite our empathy and familiarity with the human emotional experience, we are only able to guess what the other might be feeling. In the fields of artificial intelligence and computer vision, Facial Emotion Recognition (FER) is a topic that is still in full growth mostly with the advancement of deep learning approaches and the improvement of data collection. The main purpose of this paper is to compare the performance of three state-of-the-art networks, each having their own approach to improve on FER tasks, on three FER datasets. The first and second sections respectively describe the three datasets and the three studied network architectures designed for an FER task. The experimental protocol, the results and their interpretation are outlined in the remaining sections.